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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknologi Informasi dan Ilmu Komputer English Review: Journal of English Education Jurnal Pilar Nusa Mandiri JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah EXPLORE Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Infotek : Jurnal Informatika dan Teknologi JURNAL REKAYASA INFORMASI SWADHARMA (JRIS) Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Journal of Computer Science and Informatics Engineering EXPLORE English Education and Applied Linguistics Journal (EEAL Journal) Journal of Computer Science and Information Technology Journal of Data Analytics, Information, and Computer Science (JDAICS) Sasambo: Jurnal Abdimas (Journal of Community Service)
Subki, Ahmad
Universitas Teknologi Mataram
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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APLIKASI PENGENALAN PARIWISATA DESA WISATA SESAOT BERBASIS ANDROID Arwidiyarti, Dwinita; Subki, Ahmad
JRIS : Jurnal Rekayasa Informasi Swadharma Vol 5, No 2 (2025): JURNAL JRIS EDISI JULI 2025
Publisher : Institut Teknologi dan Bisnis (ITB) Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jris.vol5no2.769

Abstract

This study aims to develop an introduction application for the Sesaot Tourism Village in Narmada District, West Lombok, West Nusa Tenggara. This qualitative descriptive study uses the waterfall system development method and system design tools using UML. Data collection was carried out using observation and interview techniques. The study results are in the form of an Android-based Sesaot Tourism Village application that has undergone functionality testing using the Black Box Testing method with 100% valid results, which means that all menus and buttons can function correctly. With this application, tourists can easily get information related to Sesaot Tourism Village, both tourist attractions equipped with maps and routes, events that will be held, ticket purchases, and sales of superior UMKM products from Sesaot Tourism Village. The impact of the research is expected to increase the number of tourist visits and expand the marketing area of Sesaot Tourism Village to national and international levels.Tujuan penelitian adalah untuk membangun aplikasi pengenalan Desa Wisata Sesaot di Kecamatan Narmada, Lombok Barat, Nusa Tenggara Barat. Penelitian ini merupakan penelitian deskriptif kualitatif dengan metode pengembangan sistem Waterfall dan alat perancangan sistem menggunakan UML. Pengumpulan data dilakukan dengan teknik observasi dan wawancara. Hasil penelitian berupa aplikasi Desa Wisata Sesaot berbasis Android yang telah dilakukan pengujian fungsionalitas menggunakan metode Black Box Testing dengan hasil 100% valid, yang artinya semua menu dan button dapat  berfungsi dengan baik. Dengan adanya aplikasi ini wisatawan dengan mudah mendapatkan informasi terkait Desa Wisata Sesaot, baik objek wisatanya yang dilengkapi dengan peta dan rute, acara-acara yang akan dilaksanakan, pembelian tiket dan penjualan produk-produk UMKM unggulan desa wisata Sesaot. Dampak penelitian diharapkan dapat meningkatkan jumlah kunjungan wisatawan dan memperluas area pemasaran Desa Wisata Sesaot ke tingkat nasional dan mancanegara.
PRAGMATIC POWER OR PERIL? INDONESIAN STUDENTS RESPOND TO CHATGPT'S LINGUISTIC SUBTLETIES IN ARGUMENTATIVE WRITING Nurjamin, Lucky; Maulana, Risal; Nurjamin, Asep; Subki, Ahmad; Damayanti, Hemalia
English Review: Journal of English Education Vol. 13 No. 2 (2025)
Publisher : University of Kuningan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/erjee.v13i2.11840

Abstract

This study investigates Indonesian university students' responses to the pragmatic meaning conveyed by ChatGPT in the context of argumentative writing. Employing a qualitative approach, the research explores students' perceptions of the ease of understanding and utilizing the pragmatic cues embedded in AI-generated text, their attitudes toward using the tool, and their behavioral intention for continued use. Findings reveal that while students appreciate ChatGPT's usefulness and ease of use for generating argumentative content, their engagement with its pragmatic meaning is more nuanced. Students often modify AI-generated language to align with their own writing styles and perceived academic appropriateness, indicating a level of critical awareness. A writer's stance and attitude towards the argument are often conveyed through pragmatic choices. Students' modifications could reflect an effort to inject their own voice and perspective, which might be absent or inadequately represented in the AI-generated text.
A Hybrid Framework Based on YOLOv8 and Vision Transformer for Multi-Class Detection and Classification of Coffee Fruit Maturity Levels Subki, Ahmad; M. Zulpahmi; Imran, Bahtiar
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10590

Abstract

Detection and classification of coffee cherries based on maturity levels present a significant challenge in agricultural product processing systems, primarily due to the high visual similarity among classes within a single bunch. This study aims to develop a multi-class detection and classification system for coffee cherries by integrating YOLOv8 and Vision Transformer (ViT) as a classification enhancer. The initial detection process is conducted using YOLOv8 to identify and automatically crop coffee cherry objects from bunch images. These cropped images are then re-classified using the Vision Transformer to improve prediction accuracy. The training process was carried out with a learning rate of 0.0001, a batch size of 16, and epoch variations of 50, 100, and 150. Evaluation results demonstrate that the integration of YOLOv8 and ViT significantly improves classification accuracy compared to using YOLOv8 alone. At 100 epochs, the YOLOv8+ViT model achieved an accuracy of 89.52%, a precision of 90.43%, and a recall of 89.52%, outperforming the standalone YOLOv8 model, which only reached an accuracy of 75.44%. These results indicate that the Vision Transformer effectively enhances classification performance, particularly for visually similar coffee cherry classes. The integration of these two methods offers a promising alternative solution for improving image-based multi-class classification in agriculture and other domains involving complex visual objects.
IoT-Based Smart Parking: Slot Detection and Vehicle Navigation in Parking Buildings Sapitri, Nurul; Samsumar, Lalu Delsi; Subki, Ahmad
Journal of Computer Science and Informatics Engineering Vol 4 No 4 (2025): October
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v4i4.1262

Abstract

Problems in conventional parking systems are still frequently encountered, such as difficulties in finding vacant slots and the lack of real-time information for drivers. This study aims to develop an IoT-based Smart Parking system capable of detecting parking slot availability and providing automatic voice navigation for drivers within parking buildings. The research method employed is prototyping, consisting of requirement analysis, system design, prototype development, and testing stages. The system utilizes ESP32 as the main microcontroller, infrared and ultrasonic sensors for vehicle detection, and a DFPlayer Mini module for voice navigation. Parking information is displayed in real-time on an LCD screen and the Blynk application via a WiFi connection. The test results show that the system achieved an average response time of 1.57 seconds, an accuracy rate of 94.7%, and stable performance under various test conditions. The components used are cost-effective, with an estimated prototype cost of Rp600,000–Rp1,000,000 per unit, making it feasible for real implementation in parking buildings. The system has proven to be responsive, efficient, and practical, and can serve as an innovative solution for modern parking management based on IoT technology and smart navigation
Membandingkan Tingkat Kemiripan Rekaman Voice Changer Menggunakan Analisis Pitch, Formant Dan Spectogram Subki, Ahmad; Sugiantoro, Bambang; Prayudi, Yudi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 1: Februari 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (498.29 KB) | DOI: 10.25126/jtiik.201851500

Abstract

Audio forensik merupakan salah satu ilmu yang mnyandingkan antara ilmu pengetahuan dan metode ilmiah dalam proses analisis rekaman suara untuk membantu dan mendukung pengungkapan suatu tindak kejahatan yang diperlukan dalam proses persidangan. Undang-undang ITE No.19 Tahun 2016 menyebutkan bahwa rekaman suara merupakan salah satu alat bukti digital yang sah dan dapat digunakan sebagai penguat dakwaan. Rekaman suara yang merupakan barang bukti digital sangatlah mudah dan rentan dimanipulasi, baik secara sengaja maupun tidak disengaja. Pada penelitian ini dilakukan analisis terkait tingkat kemiripan antara rekaman suara voice changer dengan rekaman suara asli menggunakan analisis pitch, formant dan spectogram, rekaman suara yang dianalisis ada dua jenis rekaman suara yaitu suara laki-laki dan suara perempuan. Rekaman suara voice changer  dan rekaman suara asli, diekstrak menggunakan tools praat kemudian informasi yang diperoleh dianalisis dengan analisis statistik pitch, formant dan spectrogrammenggunakan tools gnumeric. Penelitian ini menghasilkan bahwa analisis rekaman suara voice changer dengan rekaman suara asli dapat menggunakan analisis statistik pitch, formant dan spectrogram, rekaman suara voice changer A memiliki tingkat kemiripan yang paling tinggi dengan rekaman suara asli pada posisi low pitch, sedangkan voice changer yang lain lebih sulit untuk diidentifikasi.
Anomaly-Based DDoS Detection Using Improved Deep Support Vector Data Description (Deep SVDD) and Multi-Model Ensemble Approach Imran, Bahtiar; Samsumar , Lalu Delsi; Subki, Ahmad; Wahyuni, Wenti Ayu; Muahidin, Zumratul; Karim, Muh Nasirudin; Yani, Ahmad; M. Zulpahmi
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11863

Abstract

Distributed Denial-of-Service (DDoS) attacks remain a critical threat to network infrastructure, demanding robust and efficient detection mechanisms. This study proposes an enhanced Deep Support Vector Data Description (Deep SVDD) model for unsupervised DDoS detection using the UNSW-NB15 dataset. The approach leverages a deep encoder architecture with batch normalization and dropout to learn compact latent representations of normal traffic, minimizing the hypersphere volume enclosing benign flows. Only normal samples are used during training, adhering to the unsupervised anomaly detection paradigm. The model is evaluated against five established baselines—Isolation Forest, Local Outlier Factor (LOF), One-Class SVM, Autoencoder, and a simple ensemble—using AUC, F1-score, and recall as primary metrics. Experimental results demonstrate that Deep SVDD significantly outperforms all baselines, achieving superior class separation, high detection sensitivity, and computational efficiency (0.0004 GFLOPs). Notably, while LOF exhibited a deceptively high F1-score, its AUC near 0.5 revealed poor discriminative capability, highlighting the risk of relying on single metrics. The ensemble approach failed to improve performance, underscoring the limitation of naive score averaging when weak detectors are included. Visualization of score distributions and ROC curves further confirms Deep SVDD’s ability to effectively distinguish DDoS from benign traffic. These findings affirm that representation learning in latent space offers a more reliable foundation for anomaly detection than traditional distance-, density-, or reconstruction-based methods. The proposed model presents a promising solution for real-time, low-overhead intrusion detection systems in modern network environments. Future work will explore adaptive ensembles, self-supervised pretraining, and deployment on edge devices.
Co-Authors - Bambang Sugiantoro, Bambang Sugiantoro - Salman Achmad Syauqi Agus Hermanto Ahmad Yani ahmad yani Akbar , Ardiyallah Akbar, Ardiyallah Arwidiyarti, Dwinita Asep Nurjamin Bambang Sugiantoro Damayanti, Hemalia Efendi, Muhammad Masjun Erfan Wahyudi erniwati, surni Galih, Thomas Hambali Hambali Hidayat, Ery Rizal Hulaila, Rika Imran, Bahtiar Karim, Muh Nasirudin Kristy, Rhama Aziz Aulia Lalu Delsi Samsumar, M.Eng. M. Zulpahmi Maulana, Risal Moh. Subli Muahidin, Zumratul Muhammad Multazam Muhammad Rijal Alfian Muhammad Zohri, Muhammad Nurjamin, Lucky Patimah, Euis Rika Hulaila Salman Salman Salman Salman Salman, - Sapitri, Nurul Supardianto Supardianto Thomas Galih Tony Gunawan wahyuni, wenti ayu Yudi prayudi Zaenaldi, Firki Zaeniah Zaeniah Zulpahmi, M