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All Journal Urania Jurnal Ilmiah Daur Bahan Bakar Nuklir Majalah Ilmiah Bijak Jurnal Sains dan Teknologi Journal Sensi: Strategic of Education in Information System Jurnal Sistem Komputer & Kecerdasan Buatan Infotech: Journal of Technology Information Jurnal Teknologi Informatika dan Komputer Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Abdi Masyarakat Humanis PASCIDEV: Pasundan Social Science Development AME (Aplikasi Mekanika dan Energi) : Jurnal Ilmiah Teknik Mesin Jurnal Ilmiah Multidisiplin Indonesia Jabdimas: Jurnal Pengabdian Masyarakat E-Amal: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmu Komputer
Taswanda Taryo
Center for Development of Research Reactor Technology-BATAN, Kawasan Puspiptek, Gedung 31, Serpong 15310, Tangerang, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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ANALISIS SENTIMEN OPINI PENGGUNA JASA PENGIRIMAN JNE MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER DAN K-NEAREST NEIGHBORS Halimatussadiah, Siti; Tukiyat, Tukiyat; Taryo, Taswanda
Infotech: Journal of Technology Information Vol 11, No 1 (2025): JUNI
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i1.358

Abstract

JNE's high-quality service will provide optimal satisfaction to users, ensuring they feel valued and have a reliable and efficient delivery experience. To provide optimal service, this research explores in-depth user sentiment analysis of freight forwarding applications in Indonesia. The purpose of the study is to analyze user sentiment towards the My JNE app, which is one of the leading freight forwarding apps in Indonesia. This research uses user review data from Google Play Store collected from 2018 to 2024. The review sentiment is categorized into positive, neutral, and negative using the VADER analysis tool. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool specifically designed to detect sentiment in social media text. After the data reduction process, neutral sentiment classes were removed to focus the analysis on two main categories: positive and negative. Of the total 5,000 review samples analyzed, it was found that 35.78% belonged to the positive category and 64.21% to the negative category. The classification methods used in this study are Naïve Bayes and K-Nearest Neighbors (KNN). The analysis results show that the Naïve Bayes model has an accuracy of 81.64%, while K-Nearest Neighbors (KNN) has an accuracy of 76.25%. This accuracy test confirms that the KNN model is more effective in classifying user sentiment compared to Naïve Bayes. The results of this study provide important insights into user perceptions of the My JNE application, which can be used as a basis for improving service quality in the future. This research suggests that My JNE focus on improving features that often receive negative reviews to increase user satisfaction.
ANALISIS DAN PENGEMBANGAN SISTEM MONITORING RADIOAKTIF ALAMIAH RADON MENGGUNAKAN DETEKTOR SINTILASI BERBASIS WEB SECARA REAL TIME Saepudin, Asep; Madinah, Dzahra Al; Makhsun, Makhsun; Taryo, Taswanda
Infotech: Journal of Technology Information Vol 11, No 1 (2025): JUNI
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i1.384

Abstract

Monitoring radon gas concentrations in various environments, such as residential areas, buildings, caves, and mining sites, is crucial to minimizing health risks associated with radon exposure exceeding the 100 Bq/m³ threshold set by the World Health Organization (WHO). Additionally, anomalies in radon concentration in fault zones are often considered precursors to seismic activity. Therefore, this study develops a real-time Internet of Things (IoT)-based radon gas monitoring system using a cost-effective approach. The system utilizes a ZnS(Ag)-based scintillation detector combined with a Photo Multiplier Tube (PMT) model H10492-001 (Hamamatsu, Japan). Calibration results at the Geological Resource Research Center – BRIN Bandung indicate that the detector has an average efficiency of 79.8%. The cloud-based monitoring system is developed using PHP 8.0 and MySQL 10.5, with performance evaluation conducted through an API using the GET method via the cURL application. Testing with various intervals and iterations shows that the system achieves 99% data reception and recording efficiency compared to the data sent by the test device. Performance testing using Chrome DevTools indicates a response time ranging from 32–140 ms, demonstrating that the system responds quickly and efficiently handles user requests. The system includes an early warning mechanism that activates when sensor data exceeds a predefined threshold, featuring a red indicator on the dashboard, an alarm sound, and automatic notifications to a Telegram bot. Responsiveness testing confirms that the dashboard display adapts optimally to various screen sizes, ensuring accessibility across multiple devices. From a cybersecurity perspective, the system implements HTTPS protocols and has received an A rating from www.ssllabs.com. It also employs BCrypt encryption with a 184-bit hash length for password protection.
Implementation of Convolutional Neural Networks for Eyeglass Product Image Retrieval: A Comparative Study of ResNet-50 and MobileNetV2 Taufik, Handri; Anggai, Sajarwo; Taryo, Taswanda
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 5 No. 02 (2026): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), February 2026
Publisher : Sean Institute

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Abstract

The increasing similarity among eyewear product designs poses significant challenges for conventional text-based search systems, highlighting the need for effective Content-Based Image Retrieval (CBIR) approaches. This study proposes a CNN-based CBIR system for eyeglass frame and sunglasses retrieval, employing a comparative analysis of ResNet50 and MobileNetV2 as feature extractors. The dataset comprises 4,500 gallery images and 300 query images, with feature similarity measured using cosine similarity and accelerated through FAISS indexing. Experimental results indicate that ResNet50 achieves higher recall (0.0622), demonstrating its ability to capture more complex visual features. In contrast, MobileNetV2 provides superior ranking performance, achieving an mAP of 0.6091 and an MRR of 0.1427, outperforming ResNet50 (mAP of 0.5019 and MRR of 0.0713), while also reducing feature extraction time (0.1348 s versus 0.2023 s). These findings suggest that ResNet50 is more suitable for accuracy-oriented retrieval tasks, whereas MobileNetV2 is better suited for real-time and resource-constrained applications.
Analisa Backup Dan Replikasi Virtual Server Dengan Menggunakan Penjadwalan Koneksi Data Pada Disaster Recovery Center (DRC) Pratama, Bayu Putra; Taryo, Taswanda; Hindansyah, Achmad
Jurnal Ilmu Komputer Vol 4 No 1 (2026): Jurnal Ilmu Komputer (Edisi Januari 2026)
Publisher : Universitas Pamulang

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Abstract

Server backup and replication are critical components in ensuring operational continuity and disaster recovery within modern information systems. One of the main challenges faced is the unscheduled transfer of data between the Data Center (DC) and the Disaster Recovery Center (DRC), which can lead to the spread of viruses, malware, and ransomware into the DRC environment, thereby disrupting the recovery process. This study aims to analyze the effectiveness of data connection scheduling in the backup and replication of virtual servers at PT XYZ Finance. The research adopts a quantitative approach by measuring system performance before and after the implementation of scheduled replication. Quantitative parameters include replication time, volume of successfully transferred data, backup success rate, and recorded security incidents. Data were collected through direct system testing and analyzed using descriptive and comparative statistical methods. The results show that implementing a structured data connection schedule in the replication system significantly supports faster operational recovery and reduces the number of security incidents impacting the DRC. Based on these findings, scheduled data connections in server replication have proven to be quantitatively effective in improving system efficiency and security. Therefore, this approach is recommended as part of a data-driven disaster recovery strategy in enterprise IT environments.
Comparison of Faster R-CNN and YOLO v12 on Passport Text Extraction Based on Optical Character Recognition Samosir, Masniari; Anggai, Sajarwo; Taryo, Taswanda
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3307

Abstract

Current developments in information technology are driving the need for digitalization of official identity documents, including passports, to improve service efficiency and reduce reliance on manual processes. The digitalization of official identity documents such as passports still faces efficiency and accuracy challenges due to manual data entry processes. This study aims to compare the performance of Faster R-CNN and YOLO v12 in an automatic text extraction system based on Optical Character Recognition (OCR). The research employed an experimental method with a comparative approach using 31 preprocessed passport images. YOLO v12 was integrated with EasyOCR, while Faster R-CNN was combined with a PyTorch-based OCR module. The evaluation metrics included mAP, Character Accuracy Rate (CAR), Word Error Rate (WER), F1-score, and inference time. The results indicate that YOLO v12 outperforms Faster R-CNN in object detection, achieving an mAP@50 of 95.0% and mAP@50–95 of 90.0%, compared to 93.0% and 89.0%, respectively. In terms of text extraction accuracy, Faster R-CNN achieved a CAR of 50.01% and an F1-score of 55.75%, slightly higher than YOLO v12 with a CAR of 47.72% and an F1-score of 53.84%. However, YOLO v12 produced a lower WER and faster inference time of 2.4202 seconds (0.45 FPS). The findings suggest that YOLO v12 excels in efficiency and detection performance, while Faster R-CNN performs better in specific text extraction accuracy.
Perancangan Sistem PLTS Hybrid di Laboratorium Fisika Dasar Universitas Ibn Khaldun Bogor Mustopa, Opa; Simamora, Rezeki; Taryo, Taswanda; Yuana, Rivira; Wasitova, Lilly S
AME (Aplikasi Mekanika dan Energi): Jurnal Ilmiah Teknik Mesin Vol. 12 No. 1 (2026)
Publisher : Universitas Ibn Khaldun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/ame.v12i1.2358

Abstract

Permasalahan ketersediaan energi listrik dan meningkatnya kebutuhan energi di lingkungan pendidikan mendorong pencarian sumber energi alternatif yang bersih dan berkelanjutan. Penelitian ini bertujuan untuk merancang sistem Pembangkit Listrik Tenaga Surya (PLTS) sebagai solusi energi penerangan mandiri di Laboratorium Fisika Dasar Universitas Ibn Khaldun Bogor. Metode yang digunakan mencakup analisis kebutuhan energi, potensi radiasi matahari (dengan rata-rata 5 jam Peak Sun Hour per hari di Bogor), dan perhitungan kapasitas serta jumlah modul surya berdasarkan efisiensi sistem sebesar 80%. Berdasarkan konsumsi energi harian sebesar 11.320 Wh, sistem PLTS dirancang menggunakan 10 modul surya berkapasitas 290 Wp, yang dikonfigurasikan dalam 2 array. Sistem juga mencakup inverter hybrid, KWH Exim. Instalasi ini mampu menghasilkan daya sebesar ±2213,25 W dan beroperasi dengan tegangan ±220V DC, yang kemudian diubah menjadi AC melalui inverter  berkapasitas 3.000 W. Berdasarka hasil perhitungan performance ratio sebesar 76% maka sistem ini dinyatakan layak  dan memiliki nilai edukatif karena berfungsi sebagai laboratorium hidup untuk mendukung pembelajaran energi terbarukan bagi mahasiswa.
Co-Authors Achmad Hindasyah, Achmad Adhyaksa Waskita, Arya Agung Budi Susanto Agustian, Rizki Anggai, Sajarwo B., Hamsinah Baharuddin, Hamsinah Bayu Putra Pratama, Bayu Putra Budi Susanto, Agung Geni Rina Sunaryo Halimatussadiah, Siti Hamsinah Hamsinah, Hamsinah Hilman, Amirul Hindansyah, Achmad iman, Fajar nur Madinah, Dzahra Al Makhsun, Makhsun Mardiyanto Mardiyanto Meniek Rachmawati Mustopa, Opa Nafiah, Nafiah Ridwan, Ridwan Rizaldi, Devan Rusilowati, Umi Saepudin, Asep Salsabilah, Zafira Samosir, Masniari Sarwani Sarwani, Sarwani Sekar Putri, Utari Sholihin, Dwi Rahmad Simamora, Rezeki Sunardi, Nardi Sunarsi, Denok Syarifudin Syarifudin Taufik, Handri Teguh Prasetyo Tukiyat Tukiyat, Tukiyat Wasitova, Lilly S Yuana, Rivira