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All Journal JURNAL INSTEK (Informatika Sains dan Teknologi) Musamus Journal of Technology & Information Jurnal Info Sains : Informatika dan Sains Journal of Computer Engineering: Progress, Application and Technology Jurnal Sistem Informasi Journal of Embedded Systems, Security and Intelligent Systems Riwayat: Educational Journal of History and Humanities Journal of Multidisciplinary Academic Business Studies
JAROT BUDIASTO
Jurusan Sistem Informasi, Fakultas Teknik, Universitas Musamus Merauke
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences

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PEMANFAATAN TEKNOLOGI ANDROID SEBAGAI MEDIA ALAT PENDETEKSI KEBAKARAN BERBASIS ARDUINO SELFINA PARE; TATIK M. TALLULEMBANG; JAROT BUDIASTO
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 7 No 1 (2022): APRIL
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v7i1.27688

Abstract

Salah satu inovasi yang menerapkan IoT adalah smarthome atau rumah pintar. Contoh penerapan IoT dalam bidang keamanan modern adalah alat pendeteksi pada kebakaran dari jarak jauh memanfaatkan smartphone android sebagai media informasi dan rangkaian Arduino UNO sebagai alat identifikasinya. Perancangan sistem yang digunakan dalam penelitian ini adalah Pemodelan Use Case Diagram with UML dan menggunakan software Android Studio, Arduino IDE, dan Firebase Realtime Database. Pengujian terhadap fungsionalitas alat dan sistem menggunakan metode Black-box dan pengujian prototype. Penelitian ini menghasilkan perangkat dan sistem yang mampu mendeteksi terjadinya kebakaran pada bangunan kosong atau sedang tidak berpenghuni berdasarkan identifikasi tanda-tanda kebakaran dari sensor thermal, sensor asap, dan sensor suhu, Kemudian mengirimkan informasi tersebut sebagai notifikasi peringatan pada smartphone android dan didapatkan jarak optimal sensor bekerja Kata kuci : Internet of Things (IoT), Aplikasi Pendeteksi Kebakaran, Android
Storybook validation: Essential practices for student's financial literacy Irianto, Okto; Susanto, Susanto; Asmaningrum, Henie Poerwandar; Rachman, Adi Maulana; Budiasto, Jarot; Sokheh, Habib
Journal of Multidisciplinary Academic Business Studies Vol. 1 No. 2 (2024): February
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jomabs.v1i2.3398

Abstract

Purpose: To develop and validate a financial literacy storybook for Junior High School students using the 4D (Define, Design, Develop, Disseminate) method. Research Methodology: This study employs a development research approach, specifically utilizing the 4D (Define, Design, Develop, Disseminate) method to create and validate a financial literacy storybook for Junior High School students. Results: High feasibility scores from both material (72 points) and media (77 points) experts validated the storybook's content accuracy, relevance, and overall design quality. These results suggest that the approach of using digital storytelling, specifically through Storyjumper, can effectively bridge the gap between abstract financial concepts and relatable age-appropriate narratives for adolescents. Conclusions: The validated storybook enhances students’ financial literacy by combining pedagogical accuracy with engaging design, and digital storytelling innovatively boosts understanding and interest in financial education. Limitations: First, the validation process relied primarily on expert opinions, which, although valuable, may not fully capture the perspectives of the target audience, Junior High School students. The study did not include a pilot test with actual students, which could provide insights into the storybook's real-world effectiveness and engagement levels. Additionally, the research is limited to a specific geographical and cultural context, potentially affecting the generalizability of the findings to other regions or educational systems. Contribution: This research contributes a validated financial literacy storybook for Junior High School students, advancing educational tools in this crucial field and demonstrating the 4D method's effectiveness in educational material development.
AI-Driven Mental Health Assessment: Evaluating the Efficacy of Machine Learning in Detecting Depression and Anxiety from Digital Behavioral Data Jarot Budiasto
Riwayat: Educational Journal of History and Humanities Vol 8, No 2 (2025): April
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jr.v8i2.45375

Abstract

The integration of artificial intelligence (AI) in mental health assessments has emerged as a promising approach to improve early detection and intervention against psychological disorders. This study evaluates the effectiveness of machine learning (ML) models in detecting depression and anxiety through digital behavioral data, such as social media activity, speech patterns, and biometric signals. Using both supervised and unsupervised learning techniques, we analyzed large-scale datasets to identify behavioral markers associated with mental health conditions. The findings show that ML-based models achieve high levels of accuracy in predicting depression and anxiety, surpassing traditional self-report methods in terms of sensitivity and specificity. Additionally, the study highlights ethical considerations in the application of AI in the field of mental health, including privacy issues, algorithmic bias, and clinical validation. The study contributes to the increasing scientific evidence regarding AI-based mental health assessments and confirms the importance of responsible implementation in clinical settings and digital health. Future research needs to focus on improving model interpretability, improving generalizations in diverse populations, as well as integrating AI-based assessments into real-world mental health care systems.
Accuracy–Efficiency Trade-off Analysis of Five Lightweight CNN Architectures for Mobile-Deployable Corn Leaf Disease Classification Jarot Budiasto; Hasanudin Jayawardana; Tri Kustanti Rahayu; Tatik Melinda Tallulembang
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.12264

Abstract

Purpose – Corn leaf disease diagnosis in resource-constrained agricultural settings requires mobile-deployable models that maintain a practical balance between classification accuracy, model size, and on-device latency. This study aims to provide empirical guidance for selecting lightweight Convolutional Neural Network (CNN) architectures by systematically analyzing the accuracy–efficiency trade-offs of five models for corn leaf disease classification. Design/methods/approach – MobileNetV2, MobileNetV3-Small, MobileNetV3-Large, EfficientNetB0, and NASNetMobile were evaluated on the PlantVillage Corn dataset comprising 4,188 images across four classes under identical experimental settings. The models were trained using a two-phase strategy and converted into standard and dynamic-range quantized TensorFlow Lite formats. Evaluation covered classification accuracy, macro F1-score, model size, Android on-device inference latency, Pareto frontier and radar analyses, and pairwise McNemar's tests with Yates continuity correction. Findings – EfficientNetB0 achieved the highest accuracy (95.25%) and macro F1-score (93.77%). MobileNetV3-Small offered the strongest efficiency under the tested Android CPU setting, reaching 94.54% accuracy with a 1.18 MB dynamic-range quantized TensorFlow Lite model and 3.89 ± 0.04 ms standard inference. The top three models were statistically comparable (p = 0.6625-1.0000). Research implications/limitations – Standard TensorFlow Lite is preferable for low-latency Android CPU deployment, whereas dynamic-range quantized TensorFlow Lite supports storage-constrained offline distribution. However, the findings are limited to the PlantVillage benchmark dataset and testing on a single mid-range Android device. Originality/value – This study integrates lightweight CNN benchmarking, TensorFlow Lite deployment, real-device Android testing, accuracy–efficiency trade-off analysis, and statistical validation to support evidence-based mobile agricultural AI model selection.
Co-Authors Adi Maulana Rachman Agustan Latif Alfian, Rakhmat Alifia Mutiara Putri Astria Sarman Buono, Lintang Cahyo Diana, Firmansyah Farisya Setiadi Ferika Anjani K. Dewi Fransiskus Xaverius Gunawan, Refangga C. Hasanudin Jayawardana Hasanudin Jayawardana Hebrian S. Pandera Henie Poerwandar Asmaningrum, Henie Poerwandar Henry Soleman Raubaba, Henry Soleman Hilman, Muhammad Hafizhuddin Ika Sarika Ika Sarika Indah Purnamasari Indri Irmasari Jayawardana, Hasanuddin Jayawardana, Hasanudin Kumbiningsih Kumbiningsing Loppies, Stanly H.D. Manggala, Fajar Wira Cakra Muh. Hasbi Muhammad Hafizhuddin Hilman Muhammad Hafizhuddin Hilman Muhammad Hasbi Nasra Pratama Putra Nathalis Yordan Mathius Okto Irianto, Okto Patrisa K.P. Balubun Rakhmat Alfian Rakhmat Alfian Reza Rendian Septiawan Reza Zubaedah Risma Diana Sarika, Ika Selfina Pare, Selfina Sofyan, Fauzi Sokheh, Habib Stanley HD Loppies Stanly H.D. Loppies Sulaiman Sulaiman Sumbung, Frederik H Susanto Susanto Tatik Melinda Tallulembang Tatik Melinda Tallulembang Tri Kustanti Rahayu Umar Ali Ahmad Xaverius, Fransiskus Zubaedah, Reza