JEIS: Jurnal Elektro dan Informatika Swadharma
type: channel
description: Jurnal Elektro dan Informatika Swadharma merupakan jurnal ilmiah yang diterbitkan oleh LPPM ITB Swadharma. Jurnal ini berisi tentang karya ilmiah hasil penelitian dengan topik Elektronika, Robotika, Otomasi, Kecerdasan Buatan, Teknologi Blockchain, Komputasi Awan, Arsitektur Komputer, Computer Vision, Teknologi dan Sistem Multimedia, Deep Learning, Interaksi Manusia dan Komputer, Digital Forensic, Internet of Things, IT Security, Machine Learning, Jaringan Komputer, Semantic Web, Sistem Terdistribusi, Wireless Network. Jurnal JEIS diterbitkan dua kali setahun pada bulan Januari dan Juli. P-ISSN: 2774-5775 E-ISSN: 2774-5767 pertama terbit pada Januari 2021. Jurnal JEIS memperoleh Akreditasi SINTA 4 berdasarkan Keputusan Direktur Jenderal Sains dan Teknologi Kemendikti Saintek RI No. 355/DST/D.D1/HM.01.01/2026 Tanggal 24 Juli 2026 Tentang Peringkat Akreditasi Baru Jurnal Ilmiah Periode 3 Tahun 2025 mulai Vol 5 No 2 Juli 2025 sampai dengan Vol 10 No 1 Januari 2030
publisher: Institut Teknologi dan Bisnis Swadharma
language: en-US
publication name: JEIS: Jurnal Elektro dan Informatika Swadharma
issn: 2774-5775
copyright: Penulis yang menerbitkan naskah di jurnal JEIS setuju dengan ketentuan berikut: 1. Hak cipta atas artikel dipegang oleh penulisnya. 2. Penulis memberikan kepada jurnal JEIS, hak publikasi pertama dengan karya yang secara bersamaan dilisensikan di bawah Lisensi Atribusi Creative Commons yang memungkinkan orang lain untuk berbagi karya dengan pengakuan atas kepenulisan karya dan publikasi awal dalam jurnal ini. 3. Penulis dapat masuk ke dalam pengaturan kontrak tambahan yang terpisah untuk distribusi non-eksklusif dari versi terbitan jurnal dari karya tersebut (misalnya, mempostingnya ke repositori institusi atau menerbitkannya dalam sebuah buku), dengan pengakuan awal publikasi di jurnal ini. 4. Penulis diizinkan dan didorong untuk memposting karya mereka secara online (misalnya, di repositori institusi atau di situs web mereka) sebelum dan selama proses penyerahan, karena dapat mengarah pada pertukaran yang produktif, serta kutipan yang lebih awal dan lebih besar dari karya yang diterbitkan. 5. Artikel dan materi terbitan terkait didistribusikan di bawah Lisensi Internasional Creative Commons Attribution-ShareAlike 4.0 -------------------------------------------- Authors who publish with journal JRIS agree to the following terms: 1. Copyright on any article is retained by the author(s). 2. The author grants the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal. 3. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal. 4. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their we…
items: OPTIMASI INTER-VLAN ROUTING JARINGAN WIRELESS MENGGUNAKAN METODE NETWORK DEVELOPMENT LIFE CYCLE PADA CROWN HORECA
items: ANALISA EFISIENSI WAKTU KOMPUTASI DAN PENGGUNAAN MEMORI PADA ENAM ALGORITMA PENGURUTAN
items: PERBANDINGAN EFISIENSI MEMORI DAN WAKTU KOMPUTASI ALGORITMA PENGURUTAN REKURSIF DAN ITERATIF DI PYTHON (MERGE SORT DAN QUICK SORT)
items: ANALISIS EFEKTIVITAS TEKNIK INDEXING TERHADAP WAKTU EKSEKUSI QUERY SQL PADA BASIS DATA TRANSAKSI E-COMMERCE
items: ANALISIS KOMPARATIF EFISIENSI WAKTU DAN MEMORI: LINEAR, BINARY, DAN HASH SEARCH BERBASIS C++
items: KLASIFIKASI KEMATANGAN BUAH TOMAT BERBASIS CNN DENGAN ARSITEKTUR MOBILENETV2 DAN TRANSFER LEARNING
items: PENERAPAN MACHINE LEARNING UNTUK ANALISIS PREDIKSI HARGA RUMAH MENGGUNAKAN ALGORITMA REGRESI LINIER DAN RANDOM FOREST
items: ANALISIS SENTIMEN TERHADAP AI DALAM PENDIDIKAN DI MEDIA SOSIAL MENGGUNAKAN METODE KUANTITATIF LOGISTIC REGRESSION
items: ANALISIS SENTIMEN TERHADAP CYBERBULLYING DI MEDIA SOSIAL MENGGUNAKAN METODE KUANTITATIF NAÏVE BAYES
items: DETEKSI KERUSAKAN JALAN MENGGUNAKAN CONVOLUTIONNAL NEURAL NETWORK (CNN)
items: IDENTIFIKASI JENIS TANAMAN OBAT BERDASARKAN BENTUK DAUN MENGGUNAKAN CNN
items: KLASIFIKASI KUALITAS JERUK MANDARIN BERBASIS WARNA RGB DAN HSV MENGGUNAKAN KNN
items: KLASIFIKASI KUALITAS BUAH APEL FUJI BEDASARKAN WARNA DAN BENTUK MENGGUNAKAN METODE KNN
items: PERANCANGAN INFRASTRUKTUR REDUNDANSI DENGAN MENERAPKAN HIGH AVAILABILITY UNTUK MENJAMIN KEANDALAN OPERASIONAL BISNIS
items: ANALISIS SENTIMEN PENGELOMPOKAN PENGGUNA APLIKASI PINJAMAN ONLINE DENGAN ALGORITMA K-MEANS
items: SISTEM PENGAMAN PINTU OTOMATIS DENGAN KOMBINASI RF DAN VERIFIKASI PIN BERBASIS MIKROKONTROLER
items: PURWARUPA SISTEM PERINGATAN DINI KEBAKARAN BERBASIS INTERNET OF THINGS MENGGUNAKAN MIKROKONTROLER ESP8266

OPTIMASI INTER-VLAN ROUTING JARINGAN WIRELESS MENGGUNAKAN METODE NETWORK DEVELOPMENT LIFE CYCLE PADA CROWN HORECA
type: item
description: Information technology, particularly computer networks, has become a fundamental aspect in various fields, including economics, education, communications, and entertainment. Crown Horeca already has several network devices and uses the internet. Instability often occurs in the wireless network used due to high traffic exceeding capacity. Wireless network optimisation is needed to improve efficiency and the quality of network service. The purpose of this research is to design a Virtual Local Area Network (VLAN) using Inter-VLAN Routing techniques to improve performance and network segmentation management. The study used the Network Development Life Cycle (NDLC) method with data collection techniques of direct observation, interviews, and literature review. Ping test results showed no Request Time Out (RTO), indicating stable inter-floor connections. Message delivery between devices on the network, as simulated in Packet Tracer, ran smoothly, proving that inter-VLAN communication was functioning as expected. Teknologi informasi, khususnya dalam jaringan komputer, telah menjadi aspek mendasar dalam berbagai bidang, termasuk perekonomian, pendidikan, komunikasi, dan hiburan. Crown Horeca telah memiliki beberapa perangkat jaringan dan menggunakan jaringan internet. Ketidakstabilan sering kali terjadi pada jaringan wireless yang digunakan karena tingginya trafik yang melebihi kapasitas. Diperlukan optimasi jaringan wireless guna meningkatkan efisiensi dan kualitas layanan jaringan. Tujuan penelitian untuk merancang jaringan Virtual Local Area Network (VLAN) menggunakan teknik Inter-VLAN Routing untuk meningkatkan performa dan manajemen segmentasi jaringan. Penelitian menggunakan metode Network Development Life Cycle (NDLC) dengan teknik pengumpulan data observasi langsung, wawancara dan studi pustaka. Hasil pengujian ping menunjukkan tidak adanya Request Time Out (RTO), menunjukkan koneksi antar lantai berjalan stabil. Pengiriman pesan antar perangkat di jaringan mengguna…
creator: Andy Dharmalau
creator: Lela Nurlaela
creator: Khusnul Khoiriyah
creator: Tuhfatul Habibah Hasibuan
creator: Artha Murin
rights: Copyright (c) 2026 Andy Dharmalau, Lela Nurlaela, Khusnul Khoiriyah, Tuhfatul Habibah Hasibuan, Artha Murin https://creativecommons.org/licenses/by-sa/4.0
license: https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-07
publication date: 2026-07-07
volume: 6
number: 2
starting page: 1
ending page: 9
doi: 10.56486/jeis.vol6no2.951

ANALISA EFISIENSI WAKTU KOMPUTASI DAN PENGGUNAAN MEMORI PADA ENAM ALGORITMA PENGURUTAN
type: item
description: This study aims to analyze and compare the performance of six sorting algorithms (Tim Sort, Bubble Sort, Selection Sort, Insertion Sort, Quick Sort, and Merge Sort) in terms of computational time efficiency and memory usage. Implementation was carried out in Java using Visual Studio Code as the development environment, and testing was conducted on a device with an Intel Core i7-8665U (1.02 GHz) processor, 8 GB of RAM, a 64-bit Operating System, and a 256 GB SSD. All algorithms were tested using three different dataset sizes, namely 100, 1,000, and 5,000 data points, each with three repetitions to obtain more accurate results. Based on the test, Tim Sort showed the most efficient performance, maintaining stable computational time and memory usage across all dataset sizes. Quick Sort and Merge Sort have also been shown to perform well, especially for large data, in contrast to Bubble Sort, Selection Sort, and Insertion Sort, which become less efficient as data grows due to significantly increased processing time. Overall, this study concludes that Tim Sort is the most optimal algorithm in terms of speed and memory efficiency. Quick Sort and Merge Sort are recommended for sorting large-scale data. At the same time, basic algorithms like Bubble Sort, Selection Sort, and Insertion Sort are better suited to small datasets or to learning sorting concepts. Penelitian ini bertujuan untuk manganalisis dan membandingkan performa enam algortima sorting (Tim Sort, Bubble Sort, Selection Sort, Insertion Sort, Quick Sort, dan Merge Sort) dari aspek efisiensi waktu komputasi dan penggunaan memori. Implementasi dilakukan menggunakan Bahasa pemrograman Java melalui Visual Studio Code sebagai lingkungan pengembangan, dan pengujian dijalankan pada perangkat dengan spesifikasi Intel Core i7-8665U (1,02 GHz), RAM 8 GB, Sistem Operasi 64-bit, serta SSD 256 GB. Semua algoritma diuji menggunakan tiga perbedaan ukuran dataset, yaitu 100 data, 1.000 data, dan 5.000 data, masing-masing dengan …
creator: Nafisha Putri Arsita
creator: Rahma Syarifa
creator: Tsaqib Fahmi Ahmad
creator: Imam Prayogo Pujiono
rights: Copyright (c) 2026 Nafisha Putri Arsita https://creativecommons.org/licenses/by-sa/4.0
license: https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-11
publication date: 2026-07-11
volume: 6
number: 2
starting page: 10
ending page: 21
doi: 10.56486/jeis.vol6no2.1071

PERBANDINGAN EFISIENSI MEMORI DAN WAKTU KOMPUTASI ALGORITMA PENGURUTAN REKURSIF DAN ITERATIF DI PYTHON (MERGE SORT DAN QUICK SORT)
type: item
description: This study aims to analyze and compare the computational time efficiency and memory usage of the recursive and iterative approaches in the Merge Sort and Quick Sort algorithms in Python. The method used is an experimental quantitative approach with performance testing on two data sizes, namely 10,000 and 1,000,000 random number elements. Execution time is measured using the time and timeit libraries, while memory usage is monitored using the psutil library. The results show that, in general, the iterative approach is more efficient in memory usage because it does not require a function call stack as in recursion. In terms of execution speed, recursive Quick Sort provides the fastest average time of 0.002 seconds on data of 10,000 elements, while on data of 1,000,000 elements, iterative Quick Sort shows better time performance than the recursive version. Iterative Merge Sort is consistently more memory efficient than recursive Merge Sort on both data sizes. The time difference between the recursive and iterative approaches is relatively small for medium-sized data, but memory consumption increases significantly with the recursive version as the data size increases. These findings indicate that the choice of implementation approach for sorting algorithms in Python requires considering the trade-off between speed and memory efficiency. The iterative approach is more suitable for applications with limited memory resources, while the recursive approach remains relevant for scenarios that prioritize simplicity of implementation and clarity of algorithm structure. The results of this study are expected to serve as a reference for software developers and algorithm researchers in determining optimal sorting implementation strategies in Python. Penelitian ini bertujuan menganalisis dan membandingkan efisiensi waktu komputasi dan penggunaan memori antara pendekatan rekursif dan iteratif pada algoritma pengurutan Merge Sort dan Quick Sort menggunakan bahasa pemrograman Python. …
creator: Regina Nur Aeni
creator: Alina Putri As Salwa
creator: Muhammad Farras Abiy
creator: Muhammad Noval Syafiq Sofi
creator: Imam Prayogo Pujiono
rights: Copyright (c) 2026 Regina Nur Aeni https://creativecommons.org/licenses/by-sa/4.0
license: https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-26
publication date: 2026-07-26
volume: 6
number: 2
starting page: 22
ending page: 29
doi: 10.56486/jeis.vol6no2.1073

ANALISIS EFEKTIVITAS TEKNIK INDEXING TERHADAP WAKTU EKSEKUSI QUERY SQL PADA BASIS DATA TRANSAKSI E-COMMERCE
type: item
description: This study examines the effect of indexing techniques on SQL query execution speed in a MySQL-based e-commerce transaction database. Performance issues often arise as data volume increases because the system must scan the entire table, resulting in slow response times. To analyze this problem, this study employed a pure experimental method by testing three index conditions: no index, a single index, and a composite index. Testing was conducted using a synthetic dataset of 20,000 rows depicting e-commerce transaction activity. Each condition was evaluated using a series of SELECT queries. Measurements were made through execution time and execution plans displayed by the EXPLAIN command. The results showed that using indexes provided a significant performance improvement. A single index successfully reduced execution time from 0.2380 seconds to 0.0740 seconds, while a composite index resulted in an execution time of 0.0900 seconds. Both types of indexes reduced the number of rows scanned across the entire table to only tens of rows. A single index is more suitable for simple queries with high selectivity, while a composite index is more effective for queries with more than one condition. However, implementing an index requires additional storage space and causes the data write process to run slightly slower. Overall, this study confirms that an indexing strategy tailored to query patterns and data characteristics can significantly improve the performance of e-commerce transaction databases. Penelitian ini mengkaji pengaruh teknik indexing terhadap kecepatan eksekusi query SQL pada basis data transaksi e-commerce berbasis MySQL. Gangguan kinerja sering muncul ketika jumlah data meningkat karena sistem harus melakukan pemindaian seluruh tabel yang menyebabkan waktu respon menjadi lambat. Untuk menganalisis permasalahan tersebut, penelitian ini menerapkan metode eksperimen murni dengan menguji tiga kondisi indeks yaitu tanpa indeks, indeks tunggal, dan indeks komposit. P…
creator: M Aldy Zulfa Saputra
creator: Muchammad Soufwan Fauzi
creator: Rangga Dzikri Fardiansyah
creator: Zaki Kafila Pamungkas
creator: Dicky Anggriawan Nugroho
creator: Imam Prayogo Pujiono
rights: Copyright (c) 2026 Aldy Zulfa https://creativecommons.org/licenses/by-sa/4.0
license: https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-26
publication date: 2026-07-26
volume: 6
number: 2
starting page: 30
ending page: 41
doi: 10.56486/jeis.vol6no2.1076

ANALISIS KOMPARATIF EFISIENSI WAKTU DAN MEMORI: LINEAR, BINARY, DAN HASH SEARCH BERBASIS C++
type: item
description: This study aims to analyze the comparative memory efficiency and computation time of three commonly used data search algorithms in C++ programming: Linear Search, Binary Search, and Hash Search. This study analyzes the performance of each algorithm based on two main aspects: execution speed and memory usage, to determine the most appropriate algorithm for various data conditions. The research method used was a quantitative comparative experiment, where each algorithm was implemented in C++ and tested on three data size scenarios (100, 1000, and 10,000 elements). The results obtained indicate that Binary Search has the best balance between speed and memory efficiency. At the same time, Linear Search is more suitable for small data sizes due to its simplicity of implementation. Conversely, Hash Search has high search speed but requires more memory. Therefore, the selection of the appropriate search algorithm should be tailored to the characteristics of the data and system requirements. This study is expected to serve as a reference for software developers in selecting the optimal search algorithm based on computational resource efficiency. Penelitian ini bertujuan untuk menganalisis perbandingan efisiensi memori dan waktu komputasi pada tiga algoritma pencarian data yang umum digunakan dalam pemrograman C++, yaitu Linear Search, Binary Search, dan Hash Search. Penelitian ini menganalisis kemampuan kerja pada masing-masing algoritma berdasarkan dua aspek utama, yakni kecepatan eksekusi dan penggunaan memori, agar dapat menentukan algoritma yang paling sesuai untuk berbagai kondisi data. Metode penelitian yang digunakan adalah eksperimen komparatif kuantitatif, di mana setiap algoritma diimplementasikan menggunakan bahasa C++ dan diuji pada tiga skenario ukuran data (100, 1000, dan 10000 elemen). Hasil yang diperoleh menunjukkan bahwa Binary Search memiliki kinerja paling seimbang antara kecepatan dan efisiensi memori, sedangkan Linear Search lebih cocok untuk data yang…
creator: Meila Fitri Amin
creator: Firda Rona Syahira
creator: Ikhsan Maulanaa
creator: Nevi Dwi Apriyanti
creator: Imam Prayogo Pujiono
rights: Copyright (c) 2026 firda rona syahira https://creativecommons.org/licenses/by-sa/4.0
license: https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-28
publication date: 2026-07-28
volume: 6
number: 2
starting page: 42
ending page: 51
doi: 10.56486/jeis.vol6no2.1085

KLASIFIKASI KEMATANGAN BUAH TOMAT BERBASIS CNN DENGAN ARSITEKTUR MOBILENETV2 DAN TRANSFER LEARNING
type: item
description: Classifying tomato ripeness is a crucial aspect of post-harvest processing and agricultural distribution. Manual ripeness determination is subjective and potentially inconsistencies. This study aims to classify tomato ripeness using a deep learning approach based on a Convolutional Neural Network (CNN) with the MobileNetV2 architecture and transfer learning techniques. The dataset, consisting of tomato images, was classified into three classes: unripe, ripe, and overripe. Preprocessing included image resizing and normalization before model training. The MobileNetV2 model was used as a feature extractor, while a fully connected layer was applied for classification. Test results showed that the model was able to classify tomato ripeness with good performance based on classification evaluation metrics. Overall, the proposed approach shows good potential for RGB image-based tomato ripeness classification. Klasifikasi tingkat kematangan buah tomat merupakan aspek penting dalam proses pascapanen dan distribusi hasil pertanian. Penentuan kematangan secara manual masih bersifat subjektif dan berpotensi menimbulkan ketidakkonsistenan. Penelitian ini bertujuan untuk mengklasifikasikan tingkat kematangan buah tomat menggunakan pendekatan deep learning berbasis Convolutional Neural Network (CNN) dengan arsitektur MobileNetV2 dan teknik transfer learning. Dataset berupa citra buah tomat diklasifikasikan ke dalam tiga kelas, yaitu tidak matang, matang, dan terlalu matang. Tahapan preprocessing meliputi proses resizing dan normalisasi citra sebelum dilakukan pelatihan model. Model MobileNetV2 digunakan sebagai feature extractor, sedangkan lapisan fully connected diterapkan untuk proses klasifikasi. Hasil pengujian menunjukkan bahwa model mampu mengklasifikasikan tingkat kematangan buah tomat dengan performa yang baik berdasarkan metrik evaluasi klasifikasi. Secara keseluruhan, pendekatan yang diusulkan menunjukkan potensi yang baik untuk klasifikasi tingkat kematangan buah tomat b…
creator: Mochamad Fathur Milzam
creator: Ahmad Syukri Gozali
creator: Sumanto Sumanto
creator: Jefina Tri Kumalasari
creator: Ghofar Taufiq
creator: Ade Christian
rights: Copyright (c) 2026 Mochamad Fathur Milzam https://creativecommons.org/licenses/by-sa/4.0
license: https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-28
publication date: 2026-07-28
volume: 6
number: 2
starting page: 52
ending page: 61
doi: 10.56486/jeis.vol6no2.1113

PENERAPAN MACHINE LEARNING UNTUK ANALISIS PREDIKSI HARGA RUMAH MENGGUNAKAN ALGORITMA REGRESI LINIER DAN RANDOM FOREST
type: item
description: Accurate house price prediction is crucial for property buyers, sellers, investors, and developers in making decisions. This study aims to compare the performance of Linear Regression and Random Forest algorithms in predicting house prices in East Jakarta. The dataset used was taken from the rumah123.com website, and after cleaning, there were 180 unique data points. The modelling process was carried out using the Orange application, where 80% of the data was used to train the model and 20% to test it. To assess model performance, several metrics were used, such as Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error, and the coefficient of determination. The test results showed that the Linear Regression model provided more accurate predictions than Random Forest, as evidenced by lower RMSE, MAE, and MAPE values, as well as an R² value of 0.997. This indicates that the relationship between property features and house prices in this dataset tends to be linear. However, the Random Forest model also performed well, with an R² value of 0.996, so it can still be used for datasets with more complex characteristics. Prediksi harga rumah yang tepat sangat penting untuk para pembeli, penjual, investor, dan pengembang properti dalam membuat keputusan. Penelitian ini bertujuan untuk membandingkan seberapa baik algoritma Regresi Linier dan Random Forest dalam memprediksi harga rumah di Jakarta Timur. Dataset yang digunakan diambil dari situs rumah123.com dan setelah dibersihkan, terdapat 180 data yang unik. Proses pemodelan dilakukan dengan menggunakan aplikasi Orange, di mana 80% data digunakan untuk melatih model dan 20% untuk mengujinya. Untuk menilai kinerja model, digunakan beberapa metrik seperti Mean Squared Error, Root Mean Squared Error (RSME), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), dan koefisien determinasi (R²). Hasil dari pengujian menunjukkan bahwa model Regresi Linier memberikan prediksi yang …
creator: Muhamad Fadli Fadhlullah
creator: Zayyan Nauval Araf
creator: Sumanto Sumanto
creator: Jefina Tri Kumalasari
creator: Ghofar Taufiq
creator: Ade Christian
rights: Copyright (c) 2026 Muhamad Fadli Fadhlullah https://creativecommons.org/licenses/by-sa/4.0
license: https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-28
publication date: 2026-07-28
volume: 6
number: 2
starting page: 62
ending page: 68
doi: 10.56486/jeis.vol6no2.1125

ANALISIS SENTIMEN TERHADAP AI DALAM PENDIDIKAN DI MEDIA SOSIAL MENGGUNAKAN METODE KUANTITATIF LOGISTIC REGRESSION
type: item
description: The rapid growth of Artificial Intelligence (AI) has expanded its role in education and sparked diverse public reactions on social media. This study aims to analyse sentiment toward AI in education using comments from Tempo.com's posts on the TikTok platform. A quantitative approach using Logistic Regression was applied to classify sentiment into positive and negative categories. The dataset consisted of 1,247 comments, which underwent text preprocessing, data labelling, data sharing, and model training. The analysis results showed 948 comments were negative and 298 comments were positive. The Logistic Regression model achieved an accuracy of 83%; precision 0.66, recall 0.64, and F1-score 0.65 confirm the effectiveness of this method. The research findings indicate that public perception of AI in education tends to be negative, while also providing insights for educators and policymakers to respond more wisely to technological developments. Pertumbuhan pesat Artificial Intelligence (AI) telah memperluas perannya dalam pendidikan dan memicu beragam reaksi publik di media sosial. Penelitian ini bertujuan menganalisis sentimen terhadap AI dalam pendidikan dengan menggunakan komentar dari unggahan Tempo.com di platform TikTok. Pendekatan kuantitatif dengan metode Logistic Regression diterapkan untuk mengklasifikasikan sentimen ke dalam kategori positif dan negatif. Dataset terdiri atas 1.247 komentar yang diproses melalui tahap text preprocessing, pelabelan data, pembagian data, dan pelatihan model. Hasil analisis menunjukkan 948 komentar bernuansa negatif dan 298 komentar bernuansa positif. Model Logistic Regression mencapai akurasi sebesar 83%, dengan nilai precision, recall, dan F1-score yang menegaskan efektivitas metode ini. Temuan penelitian mengindikasikan bahwa persepsi publik terhadap AI dalam pendidikan cenderung negatif, sekaligus memberikan wawasan bagi pendidik dan pembuat kebijakan untuk merespons perkembangan teknologi secara lebih bijaksana
creator: Rizki Andika Prasetyo
creator: Anindita Novelia Putri
creator: Chairul Ichwan
creator: Giantika Chrisnawati
rights: Copyright (c) 2026 Rizki Andika Prasetyo, Anindita Novelia Putri, chairul ichwan - https://creativecommons.org/licenses/by-sa/4.0
license: https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-29
publication date: 2026-07-29
volume: 6
number: 2
starting page: 69
ending page: 77
doi: 10.56486/jeis.vol6no2.1129

ANALISIS SENTIMEN TERHADAP CYBERBULLYING DI MEDIA SOSIAL MENGGUNAKAN METODE KUANTITATIF NAÏVE BAYES
type: item
description: TikTok is a digital interaction platform with a high user base. Cyberbullying is a technology-based form of bullying that can have serious impacts on the mental and social well-being of victims; therefore, understanding public perception of the issue is crucial. This study aims to identify public opinion trends regarding the current prevalence of cyberbullying. This study uses data from comments on a TikTok account and classifies these comments into positive, negative, and neutral sentiment classes. The method used is quantitative, with the naïve bayes algorithm, supported by text mining processes including data cleaning, tokenisation, stopword removal, stemming, and extraction using TF-IDF. The research dataset consisted of 1,100 comments, and after filtering, 779 comments were finalised. The test results show that naïve bayes is capable of providing good sentiment classification based on accuracy, precision, recall, and F1-score values. These findings are expected to raise awareness and minimise the occurrence of cyberbullying on social media. Media Sosial Tiktok menjadi salah satu ruang interaksi digital dengan tingkat pengguna yang tinggi. Cyberbullying merupakan perilaku perundungan berbasis teknologi yang dapat berdampak serius pada kondisi mental dan sosial korban, sehingga diperlukan pemahaman mengenai persepsi publik mengenai isu tersebut. Penelitian ini bertujuan untuk mengidentifikasi kecenderungan opini masyarakat terhadap kasus cyberbullying yang sedang marak terjadi. Penelitian ini menggunakan data dari komentar salah satu akun di TikTok, serta mengklasifikan komentar tersebut kedalam kelas sentiment positif, negatif, maupun netral. Metode yang digunakan adalah kuantitatif dengan algoritma naïve bayes, didukung oleh proses text mining mulai dari pembersihan data, tokenisasi, stopword removal, stemming, dan ekstraksi menggunakan TF-IDF. Dataset penelitian ini terdiri dari 1.100 komentar dan telah dilakukan penyaringan komentar yang berubah menjadi 779 k…
creator: Siti Hani Fadilah
creator: Aditya Fanial Rizki
creator: Khairunnisa Regita Cahyani
creator: Giantika Chrisnawati
rights: Copyright (c) 2026 Aditya Fanial Rizki https://creativecommons.org/licenses/by-sa/4.0
license: https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-29
publication date: 2026-07-29
volume: 6
number: 2
starting page: 78
ending page: 84
doi: 10.56486/jeis.vol6no2.1144

DETEKSI KERUSAKAN JALAN MENGGUNAKAN CONVOLUTIONNAL NEURAL NETWORK (CNN)
type: item
description: Road surface damage is a critical issue that can impact road user safety, transportation efficiency, and infrastructure maintenance costs. Manual road inspection methods are still widely used, but they suffer from subjective assessments that can result in inconsistent data. Therefore, an automated approach using artificial intelligence technology is needed to improve the accuracy and efficiency of road damage detection. This research aims to develop a road damage detection system using a deep learning approach based on Convolutional Neural Networks (CNN). The detection model was trained using an annotated road image dataset covering various types of road surface damage. The training and evaluation processes were conducted using an object detection architecture that included mean average precision (mAP), precision, and recall. Test results show that the CNN model can automatically detect road damage with fairly good performance. However, system performance is still affected by the limited dataset size, class imbalance, and variations in environmental conditions. The developed system has the potential to be a decision-support tool for more effective and data-driven road infrastructure maintenance. Kerusakan permukaan jalan merupakan masalah penting yang dapat memengaruhi keselamatan pengguna jalan, efisiensi transportasi, dan biaya pemeliharaan infratruktur. Metode inspeksi jalan secara manual masih banyak dipakai, memiliki kelemahan adanya subjektivitas penilaian, yang dapat menghasilkan data yang tidak konsisten. Oleh karena itu, dibutuhkan pendekatan otomatis menggunakan teknologi kecerdasan buatan untuk meningkatkan akurasi dan efisiensi deteksi kerusakan jalan. Penelitian ini bertujuan mengembangkan sistem deteksi kerusakan jalan dengan pendekatan deep learning berbasis Convolutional Neural Network (CNN). Model deteksi dilatih menggunakan dataset citra jalan yang mencakup berbagai jenis kerusakan permukaan jalan. Proses pelatihan dan evaluasi dilakukan dengan ars…
creator: Muhammad Haikal Abidin
creator: Iman Febriansya Putra
creator: Sumanto Sumanto
creator: Ade Christian
creator: Jefina Tri Kumalasari
creator: Ghofar Taufiq
rights: Copyright (c) 2026 Muhammad Haikal Abidin https://creativecommons.org/licenses/by-sa/4.0
license: https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-29
publication date: 2026-07-29
volume: 6
number: 2
starting page: 85
ending page: 94
doi: 10.56486/jeis.vol6no2.1145

IDENTIFIKASI JENIS TANAMAN OBAT BERDASARKAN BENTUK DAUN MENGGUNAKAN CNN
type: item
description: Indonesia is one of the countries with the greatest biodiversity in the world, including a wealth of medicinal plants and herbal medicines. Despite the vast potential of herbal medicinal plants, the general public still often struggles to identify and differentiate between them. This difficulty is primarily due to limited knowledge of morphological characteristics, which can lead to misidentification and the potential consumption of poisonous plants. Advances in Artificial Intelligence (AI), particularly Convolutional Neural Networks (CNN), have opened up new opportunities for identifying medicinal plant leaf images. This study aims to apply CNNs to identify medicinal plant species based on leaf shape. This study utilized the EfficientNetV2B0 architecture, known for its superior parameter efficiency and high accuracy. This model demonstrated a validation and testing accuracy of 98%. The result of this research is a mobile application that can assist the general public in identifying medicinal plants in their environment. With this application, it is hoped that the public will more easily learn the types of herbal medicinal plants, their benefits, and how to process them. This is also expected to minimize the risk of misidentification, which can negatively impact health. Indonesia merupakan salah satu negara dengan keanekaragaman hayati terbesar di dunia, termasuk kekayaan tanaman obat dan jamu. Meskipun potensi tanaman obat herbal sangat besar, masyarakat umum masih sering mengalami kesulitan dalam mengenali dan membedakan jenis-jenis tanaman obat herbal. Kesulitan ini terutama disebabkan oleh keterbatasan pengetahuan masyarakat dalam mengenali ciri-ciri morfologi, yang dapat menyebabkan risiko kesalahan identifikasi dan potensi mengkonsumsi tanaman yang beracun. Perkembangan teknologi kecerdasan buatan (AI), khususnya Convolutional Neural Network (CNN), telah membuka peluang baru dalam melakukan identifikasi citra daun tanaman obat. Penelitian ini bertujuan menerap…
creator: Meydina Aulia Savitri
creator: Trisna Andhika Saputra
creator: Aziz Bayu Permata
creator: Sumanto Sumanto
creator: Jefina Tri Kumalasari
creator: Ghofar Taufiq
rights: Copyright (c) 2026 Trisna Andhika Saputra https://creativecommons.org/licenses/by-sa/4.0
license: https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-29
publication date: 2026-07-29
volume: 6
number: 2
starting page: 95
ending page: 99
doi: 10.56486/jeis.vol6no2.1160

KLASIFIKASI KUALITAS JERUK MANDARIN BERBASIS WARNA RGB DAN HSV MENGGUNAKAN KNN
type: item
description: This study aims to automatically classify the quality of fresh and rotten Mandarin oranges using RGB and HSV color features with the K-Nearest Neighbor (KNN) algorithm. This study adopted a quantitative experimental approach. The dataset used consisted of 150 Mandarin orange images, 75 fresh and 75 rotten, with preprocessing stages consisting of image resizing to 512 × 512 pixels and feature extraction of the average RGB and HSV channel Values. Tests were conducted with varying K Values : 1, 3, 5, and 7. The results showed that the K-Nearest Neighbor method, which utilizes RGB and HSV color attributes, was able to classify the quality of Mandarin oranges effectively. The test results showed that changes in the K parameter affected the classification results, with K = 5 producing the highest accuracy of 94.74% (AP = 0.948). This indicates that the RGB and HSV color attributes with the KNN method are effective in determining the quality of Mandarin oranges. Penelitian ini bertujuan untuk mengklasifikasikan kualitas Jeruk Mandarin segar dan busuk secara otomatis menggunakan fitur warna RGB dan HSV dengan algoritma K-Nearest Neighbor (KNN). Studi ini mengadopsi metode kuantitatif dengan pendekatan eksperimen. Dataset yang digunakan terdiri dari 150 citra Jeruk Mandarin, masing-masing 75 citra Jeruk segar dan 75 citra Jeruk busuk, dengan tahap prapengolahan berupa resizing citra menjadi 512×512 piksel dan ekstraksi fitur nilai rata-rata kanal RGB dan HSV. Pengujian dilakukan dengan variasi nilai K = 1, 3, 5, dan 7. Hasil penelitian mendapatkan bahwa metode K-Nearest Neighbor yang memanfaatkan atribut warna RGB dan HSV mampu mengklasifikasikan dengan baik kualitas Jeruk Mandarin. Hasil pengujian menunjukkan bahwa perubahan nilai parameter K mempengaruhi hasil klasifikasi, di mana K = 5 menghasilkan akurasi tertinggi sebesar 94,74% dengan nilai average precision (AP) sebesar 0,948 dan memperoleh tingkat akurasi klasifikasi yang optimal. Ini menunjukkan bahwa atribut warna …
creator: Rizki Ananda Putra
creator: Maulana Ibrahim
creator: Mochammad Zulfikri
creator: Giantika Chrisnawati
rights: Copyright (c) 2026 Rizki Ananda Putra https://creativecommons.org/licenses/by-sa/4.0
license: https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-29
publication date: 2026-07-29
volume: 6
number: 2
starting page: 100
ending page: 106
doi: 10.56486/jeis.vol6no2.1190

KLASIFIKASI KUALITAS BUAH APEL FUJI BEDASARKAN WARNA DAN BENTUK MENGGUNAKAN METODE KNN
type: item
description: Determining the ripeness of Fuji apples has traditionally relied on manual visual observation, which is error-prone and time-consuming. This research aims to develop an automatic classification application using the K-Nearest Neighbor algorithm, incorporating color (RGB and HSV) and fruit shape features. The data used consisted of 48 images of Fuji apples from the Kudus region. The study resulted in an application capable of classifying ripeness into three classes: unripe, semi-ripe, and ripe, with 91.7% accuracy. Visual evaluation using a confusion matrix and sample image predictions demonstrated the application's performance as expected. Penentuan tingkat kematangan Apel Fuji selama ini masih mengandalkan pengamatan visual secara manual yang rentan kesalahan dan memakan waktu. Penelitian ini bertujuan mengembangkan aplikasi klasifikasi otomatis menggunakan algoritma K-Nearest Neighbor dengan fitur warna (RGB dan HSV) serta bentuk buah. Data yang digunakan terdiri dari 48 gambar Apel Fuji dari wilayah Kudus. Hasil penelitian menghasilkan aplikasi yang mampu mengklasifikasikan tingkat kematangan menjadi tiga kelas: mentah, setengah matang, dan matang dengan akurasi 91,7%. Hasil evaluasi visual melalui confusion matrix dan contoh prediksi gambar menunjukkan aplikasi berfungsi sesuai harapan.
creator: Dhandie Putra Kumara
creator: Syahid Muhammad Hasan Alaydroes
creator: Naufal Fathir
rights: Copyright (c) 2026 Dhandie Putra Kumara https://creativecommons.org/licenses/by-sa/4.0
license: https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-30
publication date: 2026-07-30
volume: 6
number: 2
starting page: 107
ending page: 111
doi: 10.56486/jeis.vol6no2.1192

PERANCANGAN INFRASTRUKTUR REDUNDANSI DENGAN MENERAPKAN HIGH AVAILABILITY UNTUK MENJAMIN KEANDALAN OPERASIONAL BISNIS
type: item
description: The availability of information technology services is crucial for business operations, but infrastructure with a Single Point of Failure risks causing downTime that disrupts services. This research focuses on the design and implementation of High Availability-based redundancy infrastructure to maintain service reliability when a virtualization host or network is disrupted. The research method uses the Network Development Life Cycle, which includes requirements analysis, design, simulation/prototype, implementation, monitoring, and management. The implementation was carried out in a VMware vSphere environment with two ESXi hosts managed through vCenter, and iSCSI-based NetApp shared storage as a shared datastore to support the failover mechanism and automatic VM Recovery. Testing includes host failover, network failover, and Failback scenarios with connectivity monitoring. The results show that in the host failover scenario, the virtual machine was successfully recovered automatically with 33 seconds of downTime, while in the network failover and Failback scenarios, the service remained accessible with no observed downTime (0 seconds). The conclusion of this research shows that the design and implementation of High Availability can improve service reliability, minimize downTime during disruptions, and support achieving the 99.9% SLA target in the test scenario. Ketersediaan layanan teknologi informasi sangat penting bagi operasional bisnis, namun infrastruktur dengan Single Point of Failure berisiko menyebabkan downTime yang mengganggu layanan. Penelitian ini berfokus pada perancangan dan implementasi infrastruktur redundansi berbasis High Availability untuk menjaga keandalan layanan saat terjadi gangguan pada host virtualisasi atau jaringan. Metode penelitian menggunakan Network Development Life Cycle yang meliputi analisis kebutuhan, perancangan, simulasi/prototipe, implementasi, monitoring, dan manajemen. Implementasi dilakukan pada lingkungan VMware vSphere deng…
creator: Saprila Wijaya
creator: Harjono Padmono Putro
rights: Copyright (c) 2026 Saprila Wijaya, Harjono Padmono Putro https://creativecommons.org/licenses/by-sa/4.0
license: https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-30
publication date: 2026-07-30
volume: 6
number: 2
starting page: 112
ending page: 118
doi: 10.56486/jeis.vol6no2.1234

ANALISIS SENTIMEN PENGELOMPOKAN PENGGUNA APLIKASI PINJAMAN ONLINE DENGAN ALGORITMA K-MEANS
type: item
description: The development of online lending services within financial technology has made financing easier for the public. Still, it has also given rise to problems such as high interest rates, a lack of cost transparency, and service issues that lead to user complaints. These issues are reflected in the large number of unstructured user reviews on the Google Play Store, making them difficult to analyze manually. This situation requires applying data-driven analysis methods to understand sentiment patterns and user characteristics in online lending applications. This study aims to analyze sentiment and group users of online lending applications based on Indonesian-language reviews on the Google Play Store for 2026. The methods used are data mining using a text-mining approach and unsupervised learning. The research stages include data collection, text preprocessing, feature extraction using TF-IDF, and data clustering using the K-Means algorithm with the help of Google Colab and the Orange application. The results are expected to produce user clusters with distinct sentiment characteristics and provide an overview of user perception patterns. These findings can be used as academic references and evaluation materials for online lending service providers to improve service quality. Perkembangan layanan pinjaman online sebagai bagian dari teknologi finansial telah memberikan kemudahan akses pembiayaan bagi masyarakat, namun di sisi lain memunculkan berbagai permasalahan seperti tingginya suku bunga, kurangnya transparansi biaya, serta pelayanan yang menimbulkan keluhan pengguna. Permasalahan tersebut tercermin dalam ulasan pengguna pada platform Google Play Store yang jumlahnya besar dan bersifat tidak terstruktur, sehingga sulit dianalisis secara manual. Kondisi ini menuntut penerapan metode analisis berbasis data untuk memahami pola sentimen dan karakteristik pengguna aplikasi pinjaman online. Penelitian ini bertujuan untuk menganalisis sentimen serta mengelompokkan pengguna a…
creator: Sri Ipnuwati
creator: Sabda Iman Ramadian
creator: Didi Susianto
creator: Yodhi Yuniarte
rights: Copyright (c) 2026 Sri Ipnuwati, Sabda Iman Ramadian, Didi Susianto, Yodhi Yuniarte https://creativecommons.org/licenses/by-sa/4.0
license: https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-30
publication date: 2026-07-30
volume: 6
number: 2
starting page: 119
ending page: 128
doi: 10.56486/jeis.vol6no2.1352

SISTEM PENGAMAN PINTU OTOMATIS DENGAN KOMBINASI RF DAN VERIFIKASI PIN BERBASIS MIKROKONTROLER
type: item
description: Security for enclosed spaces or buildings is a crucial concern, as conventional manual locking systems, such as physical keys, are highly vulnerable to loss, unauthorized use, or duplication. To address this key issue, this study aims to design and implement an automatic door security system that combines Radio Frequency (RF) technology and Personal Identification Number (PIN) verification using a microcontroller. This study developed the system using a prototyping method, which enables iterative testing and refinement of the hardware and software to achieve an optimal, stable system. The resulting system is a prototype automatic door security system with an ESP32 microcontroller-based main controller that processes signals from a 433 MHz RF module and validates unique PIN inputs. The primary functional requirements include an RF transmitter, RF transmitter, and servo motor to operate the lock. The resulting solution provides more reliable, secure access and minimizes the risk of unauthorized entry by requiring Two-Factor Authentication (an RF signal and a PIN). This system offers a practical, economical, and applicable security solution for homes, laboratories, and other confined spaces. Keamanan untuk ruang tertutup atau bangunan merupakan perhatian yang krusial, karena sistem penguncian manual konvensional, seperti kunci fisik, sangat rentan terhadap kehilangan, penggunaan tanpa izin, atau penggandaan. Untuk mengatasi masalah utama ini, penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem keamanan pintu otomatis yang menggabungkan teknologi Radio Frequency (RF) dan verifikasi Personal Identification Number (PIN) menggunakan mikrokontroler. Penelitian ini mengembangkan sistem menggunakan metode prototyping, yang memungkinkan pengujian berulang dan penyempurnaan perangkat keras serta perangkat lunak hingga mencapai sistem yang optimal dan stabil. Hasil penelitian berupa purwarupa sistem pengaman pintu otomatis dengan pengendali utama berbasis mik…
creator: Sujono Sujono
creator: Mochammad Rizqi Albani
rights: Copyright (c) 2026 Sujono Sujono, Mochammad Rizqi Albani https://creativecommons.org/licenses/by-sa/4.0
license: https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-31
publication date: 2026-07-31
volume: 6
number: 2
starting page: 129
ending page: 136
doi: 10.56486/jeis.vol6no2.1394

PURWARUPA SISTEM PERINGATAN DINI KEBAKARAN BERBASIS INTERNET OF THINGS MENGGUNAKAN MIKROKONTROLER ESP8266
type: item
description: Fire is a common disaster that can significantly impact human safety, damage infrastructure, and disrupt organizational activities. Delays in responding to fires can lead to greater losses. This research aims to design and build a Prototipe Internet of Things (IoT)-based fire early detection system using the ESP8266 microcontroller. The research method used in this study is the Research and Development (R&D) approach, comprising research, development, testing, and evaluation stages. The research resulted in a system designed using a DHT22 temperature and humidity sensor and a flame sensor to detect potential fires in real time. When the sensor detects extreme temperatures or a fire, the system automatically activates a buzzer as a local alert and sends a notification to the user via a Telegram bot. Testing was conducted at SMK YP IPPI Petojo. Test results showed that the system can detect potential fires with high accuracy, respond quickly, and provide warnings promptly. This Prototipe is expected to be a practical, affordable, and efficient solution for early fire hazard mitigation efforts, especially in schools and other public spaces that have limited fire safety systems. Kebakaran merupakan salah satu bencana yang sering terjadi dan dapat menimbulkan dampak yang signifikan terhadap keselamatan jiwa, kerusakan infrastruktur, serta gangguan terhadap aktivitas organisasi. Keterlambatan dalam merespons kebakaran dapat menyebabkan kerugian yang lebih besar. Penelitian ini bertujuan untuk merancang dan membangun sebuah Prototipe sistem deteksi dini kebakaran berbasis Internet of Things (IoT) yang menggunakan mikrokontroler ESP8266. Metode yang digunakan pada penelitian ini yaitu metode Research and Development (R&D) yang terdiri dari tahap research, development, testing & evaluation. Penelitian menghasilkan sistem yang dirancang menggunakan sensor suhu dan kelembaban DHT22 serta flame sensor untuk mendeteksi potensi kebakaran secara real-time. Ketika sensor mendeteksi…
creator: Ahmad Fitriansyah
creator: Izzul Islam
creator: Christine Sientta Dewi
rights: Copyright (c) 2026 Ahmad Fitriansyah, Izzul Islam, Christine Sientta Dewi https://creativecommons.org/licenses/by-sa/4.0
license: https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-31
publication date: 2026-07-31
volume: 6
number: 2
starting page: 137
ending page: 145
doi: 10.56486/jeis.vol6no2.1417