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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Teknologi International Journal of Advances in Intelligent Informatics Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Artificial Intelligence and Data Mining JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Building of Informatics, Technology and Science Journal of Applied Engineering and Technological Science (JAETS) Journal of Applied Data Sciences Charity : Jurnal Pengabdian Masyarakat Indonesian Community Journal Jurnal Pengabdian Masyarakat Bhinneka eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT)
Mahmud Dwi Sulistiyo
Universitas Telkom
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Public Health Social Sciences Other

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Multi-Temporal Factors to Analyze Indonesian Government Policies regarding Restrictions on Community Activities during COVID-19 Pandemic Fachri Pane, Syafrial; Adiwijaya, Adiwijaya; Dwi Sulistiyo, Mahmud; Akbar Gozali, Alfian
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2415

Abstract

Concerning the implementation of the government policy regarding the Restriction of Community Activities (PPKM) during the COVID-19 pandemic era, there are still discrepancies in the economic sector and population mobility. This issue emerges due to irrelevant data and information in one region of Indonesia. The data differences should be carefully solved when implementing the PPKM policy. Besides, the PPKM must also pay attention to some specific factors related to the real conditions of a region, such as the data on the epidemiology of COVID-19, economic situations, and population mobility. These three are called Multi Factors. Then, based on the data, COVID-19 has a specific spreading period that cannot be repeated and thus is called temporal. Therefore, using the Multi-Temporal Factors approach to identify their correlation with the PPKM policy by applying Machine Learning, such as the Multiple Linear Regression model and Dynamic Factors, is essential. This research aims to analyze the characteristics and correlations of the COVID-19 pandemic data and the effectiveness of the government's policy on community activities (PPKM) based on the data quality. The results show that the accuracy of the multiple linear regression models is 84%. The Dynamic Factor shows that the five most important factors are idr_close, positive, retail_recreation, station, and healing. Based on the ANOVA test, all independent variables significantly influence the dependent one. The linear multiple regression models do not display any symptoms of heteroscedasticity. Thus, based on the data quality, the implementation of PPKM by the government has a practical impact.
A Combined MobileNetV2 and CBAM Model to Improve Classifying the Breast Cancer Ultrasound Images Muhammad Rakha; Mahmud Dwi Sulistiyo; Dewi Nasien; Muhammad Ridha
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 1 (2024): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i1.4836

Abstract

Breast cancer is the main cause of death in women throughout the world. Early detection using ultrasound is very necessary to reduce cases of breast cancer. However, the ultrasound analysis process requires a lot of time and medical personnel because classification is difficult due to noise, complex texture, and subjective assessment. Previous studies were successful in ultrasound classification of breast cancer but required large computations and complex models. This research aims to overcome these shortcomings by using a lighter but more accurate model. We integrated the CBAM attention module into the MobileNetV2 model to improve breast cancer detection accuracy, speed up diagnosis, and reduce computational requirements. Gradient Weighted Class Activation Mapping (Grad-CAM) is used to improve classification explanations. Ultrasound images from two databases were combined to train, validate, and test this model. The test results show that MobileNetV2-CBAM achieves a test accuracy of 93%, higher than the complex models VGG-16 (80%), VGG-19 (82%), InceptionV3 (80%), and ResNet-50 (84%). CBAM is proven to improve MobileNetV2 performance with an 11% increase in accuracy. Grad-CAM visualization shows that MobileNetV2-CBAM can better focus on localizing important regions in breast cancer images, providing clearer explanations and assisting medical personnel in diagnosis.
Deteksi Varian Penggunaan Helm dari Kamera Surveilans Menggunakan Metode Berbasis Deep Learning Faturahman, Farhan; Yunanto, Prasti Eko; Sulistiyo, Mahmud Dwi
eProceedings of Engineering Vol. 12 No. 3 (2025): Juni 2025
Publisher : eProceedings of Engineering

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Abstract

Sepeda motor merupakan moda transportasi utama diIndonesia, tetapi tingkat kepatuhan terhadap penggunaanhelm masih rendah. Rekaman kamera surveilans yang seringkali memiliki resolusi rendah menyulitkan deteksi otomatis.Selain itu, variasi helm yang digunakan, seperti full-face,half-face, non-standar, serta pengendara tanpa helm,menjadi tantangan dalam proses pendeteksian. Penelitian inibertujuan untuk mengembangkan model deep learningberbasis YOLOv8n yang mampu mendeteksi penggunaanhelm pada citra beresolusi rendah. Dataset dikumpulkansecara mandiri dengan sudut pandang serta pencahayaanyang serupa. Pengujian dilakukan dengan berbagaikonfigurasi batch size dan jumlah epoch untuk menemukanparameter optimal. Hasil evaluasi menunjukkan bahwamodel dengan 100 epoch dan batch size 32 memberikanperforma terbaik dengan mAP@50 sebesar 0.984, mAP@50-95 sebesar 0.819, precision 0.953, recall 0.952, dan F1-score0.953. Model ini mampu mendeteksi empat kategori helmdengan akurasi tinggi meskipun pada citra beresolusi rendah.Penelitian ini membuktikan bahwa YOLOv8n dapatdigunakan untuk deteksi otomatis penggunaan helm denganakurasi tinggi, yang dapat membantu sistem pemantauan lalulintas dan meningkatkan keselamatan berkendara. Kata kunci: deteksi helm, kamera surveilans, resolusi rendah,deep learning, YOLOv8
Identifikasi Pengguna Berbasiskan Biometrik Keystroke Menggunakan MVMCNN Azzam, Muhammad Abdullah; Yunanto, Prasti Eko; Sulistiyo, Mahmud Dwi
eProceedings of Engineering Vol. 12 No. 3 (2025): Juni 2025
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Keamanan akses pengguna daring menjadi isukrusial di era digital. Identifikasi berbasis biometrik, sepertikeystroke dynamics, dianggap lebih aman dibandingkanmetode konvensional. Penelitian ini mengimplementasikanMulti-Voter Multi-Commission Nearest Neighbor Classifier(MVMCNN) untuk identifikasi pengguna melalui keystrokedynamics. MVMCNN dipilih karena kemampuannya mengatasikelemahan KNN dengan skema multi-voter dan pendekatanLocal Mean Probabilistic Neural Network (LMPNN). Datasetkeystroke dari Universitas Telkom digunakan dengan fitur UD,DD, DU, UU, dan Duration. Eksperimen meliputi tiga skenario:(1) menentukan panjang vektor optimal (N=4, 8, 12, 16, 20, 24),(2) penyederhanaan fitur menjadi rata-rata dan median, serta(3) seleksi fitur menggunakan Variance Threshold (0.1).Evaluasi menggunakan F1-Score. Hasil menunjukkan skenariopertama dengan N=20 menghasilkan F1-Score tertinggi(0.6911). Penyederhanaan fitur menurunkan performa, denganF1-Score terbaik 0.3031 (mean, k=9) dan 0.3257 (median, k=3),menandakan pentingnya kekayaan informasi dalam fitur.Seleksi fitur menggunakan Variance Threshold tidak banyakmengubah performa, menunjukkan distribusi data sudahoptimal. Temuan ini menegaskan bahwa granularitas databerperan penting dalam akurasi sistem identifikasi berbasiskeystroke dynamics. Kata kunci— biometrik, keystroke, identifikasi, mvmcnn, f1-score.
Pemutakhiran Website Jurnal Digital Sebagai Media Komunikasi dan Dokumentasi Kegiatan Siswa Pada SD Ar Rafi’ Bandung Sulistiyo, Mahmud Dwi; Sthevanie, Febryanti; Wulandari, Gia Septiana
Jurnal Pengabdian Masyarakat Bhinneka Vol. 3 No. 4 (2025): Bulan Juli
Publisher : Bhinneka Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58266/jpmb.v3i4.334

Abstract

Program pengabdian kepada masyarakat ini dilaksanakan di SD Ar Rafi’ Bandung dengan tujuan memutakhirkan website jurnal digital sebagai media komunikasi dan dokumentasi kegiatan siswa. Program ini merupakan kelanjutan dari inisiatif digitalisasi buku jurnal siswa yang telah diimplementasikan sebelumnya. Berdasarkan evaluasi dan masukan dari pihak sekolah, dilakukan pengembangan sistem secara menyeluruh guna mencakup aspek afektif dan psikomotorik siswa secara lebih komprehensif. Pemutakhiran mencakup penambahan fitur pencatatan kegiatan ekstrakurikuler, data prestasi, dan data pelanggaran siswa. Dari segi antarmuka (UI/UX), dilakukan sejumlah penyempurnaan. Fungsionalitas sistem juga ditingkatkan melalui penambahan beberapa menu. Selain itu, ditambahkan fitur ekspor laporan PDF berdasarkan rentang waktu tertentu. Melalui pengembangan ini, website jurnal digital SD Ar Rafi’ kini mampu mendukung proses dokumentasi dan pemantauan perkembangan siswa secara lebih menyeluruh, efisien, dan adaptif terhadap kebutuhan sekolah. Program ini diharapkan berkontribusi dalam mendorong transformasi digital di jenjang pendidikan dasar serta meningkatkan kolaborasi antara sekolah, guru, siswa, dan orang tua.
Advancing Vehicle Logo Detection with DETR to Handle Small Logos and Low-Quality Images Ubaidillah, Rifky Fahrizal; Sulistiyo, Mahmud Dwi; Kosala, Gamma; Rachmawati, Ema; Haryadi, Deny
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6236

Abstract

Image-based vehicle logo detection is an important component in the implementation of vehicle information recognition technology, which supports the development of intelligent transportation systems. Vehicle logos, as elements that represent the identities of vehicle brands and models, play a significant role in completing vehicle identity data. The information obtained from this logo can be utilized to solve various traffic problems, such as vehicle document counterfeiting and theft, and for better traffic planning and management purposes. However, the main challenge in developing an accurate logo detection system lies in the wide variety of shapes, sizes, and positions of logos in different types of vehicles. In addition, the generally small size of logos, especially on certain vehicles, often makes it difficult for computer-based detection systems to recognize logos consistently, thus affecting the overall performance of the detection model. In this research, the Detection Transformers (DETR) method is used to build a vehicle logo detection system that focuses on small-scale logo. The testing process was conducted using the VL-10 dataset, which was specifically designed for vehicle logo detection evaluation. The results show that the DETR model can detect vehicle logos very well, even for small-scale logos. The model achieved an AP50 value of 0.952, which indicates a high level of accuracy and reliability in detecting the vehicle logo in the dataset used.
Pengembangan Chatbot dan Pengoptimalan Mesin Pencarian untuk Meningkatkan Pemasaran dan Layanan Bisnis Lumina Indonesia Sulistiyo, Mahmud Dwi; Sthevanie, Febryanti; Wulandari, Gia Septiana
Jurnal Pengabdian Masyarakat Bhinneka Vol. 4 No. 1 (2025): Bulan September
Publisher : Bhinneka Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58266/jpmb.v4i1.416

Abstract

Pemanfaatan media sosial dan website merupakan strategi pemasaran daring yang diterapkan oleh UMKM Lumina Indonesia, yang bergerak di bidang konsultan bisnis dan kesehatan mental. Namun, strategi ini masih menghadapi kendala, terutama dalam penyampaian informasi kepada pelanggan. Banyak pelanggan mengalami kesulitan menemukan informasi terkait layanan yang diinginkan, sehingga kerap menghubungi admin secara langsung. Kondisi ini membuat admin harus menyediakan waktu dan energi ekstra untuk menjawab pertanyaan, meskipun informasi tersebut telah tersedia di website. Untuk mengatasi permasalahan tersebut, kegiatan pengabdian masyarakat ini mengusulkan dan mengimplementasikan dua solusi utama: (1) pengembangan aplikasi chatbot yang mampu memberikan respons cepat dan relevan terhadap pertanyaan pelanggan, serta (2) penerapan teknologi Search Engine Optimization (SEO) untuk meningkatkan keterlihatan dan jangkauan website di mesin pencari. Hasil kegiatan menunjukkan bahwa penerapan chatbot membantu mengurangi beban kerja admin dan mempercepat pelayanan informasi, sementara optimalisasi SEO meningkatkan jumlah kunjungan dan visibilitas website. Dengan demikian, kedua solusi ini dinilai mulai meningkatkan efektivitas pemasaran daring dan kualitas layanan Lumina Indonesia.
Peningkatan Wawasan Kecerdasan Artifisial di SMK Telkom Bandung Melalui Kegiatan Workshop Sulistiyo, Mahmud Dwi; Sthevanie, Febryanti; Wulandari, Gia Septiana
Charity : Jurnal Pengabdian Masyarakat Vol. 6 No. 1 (2023): Special Issue
Publisher : PPM Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/charity.v6i1a.5918

Abstract

Wawasan kecerdasan artifisial (AI) merupakan pengetahuan tentang teknologi dan cara kerja kecerdasan artifisial, termasuk bagaimana mesin dan sistem dapat diprogram untuk melakukan tugas-tugas yang biasanya dilakukan oleh manusia. Dengan meningkatnya penggunaan AI di berbagai bidang, termasuk industri, teknologi, dan bisnis, penting bagi siswa di SMK Telkom Bandung untuk memahami dan memiliki wawasan tentang AI. Sayangnya, sampai saat ini, SMK Telkom Bandung masih belum menerapkan materi terkait AI di dalam kurikulumnya. Salah satu cara untuk meningkatkan wawasan tentang AI di kalangan siswa SMK adalah melalui kegiatan workshop. Workshop merupakan forum yang memungkinkan siswa untuk belajar secara langsung dari para ahli, akademisi, atau praktisi di bidang terkait, dan memiliki kesempatan untuk bertanya dan berdiskusi tentang topik yang dibahas. Memahami permasalahan dan kebutuhan SMK Telkom Bandung tersebut, tim Pengabdian Masyarakat dari kelompok keahlian Intelligent System, Fakultas Informatika, Universitas Telkom mengadakan kegiatan workshop tentang wawasan AI. Kegiatan ini bertujuan untuk membantu siswa SMK Telkom Bandung dalam mempersiapkan diri menghadapi tantangan di masa depan dan berkarir di bidang yang terkait dengan AI. Serangkaian workshop diselenggarakan selama tiga hari dengan materi meliputi pengenalan dunia AI, penerapan metode AI, dan aplikasi AI yang kekinian. Materi disampaikan secara interaktif dengan selalu melibatkan peserta melalui quiz online dan penugasan di tempat. Kegiatan workshop ini mendapatkan respon yang positif, baik dari siswa-siswi maupun para guru, serta antusiasme yang tinggi untuk diadakannya workshop lanjutan tentang wawasan AI ini.
Hybrid Multi-Objective Metaheuristic Machine Learning for Optimizing Pandemic Growth Prediction Adiwijaya, Adiwijaya; Pane, Syafrial Fachri; Sulistiyo, Mahmud Dwi; Gozali, Alfian Akbar
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.981

Abstract

Pandemic and epidemic events underscore the challenges of balancing health protection, economic resilience, and mobility sustainability. Addressing these multidimensional trade-offs requires adaptive and data-driven decision-support tools. This study proposes a hybrid framework that integrates machine learning with multi-objective optimization to support evidence-based policymaking in outbreak scenarios. Six key indicators—confirmed cases, disease-related mortality, recovery count, exchange rate, stock index, and workplace mobility—were predicted using eight regression models. Among these, the XGBoost Regressor consistently achieved the highest predictive accuracy, outperforming other approaches in capturing complex temporal and socioeconomic dynamics. To enhance interpretability, we developed SHAPPI, a novel method that combines Shapley Additive Explanations (SHAP) with Permutation Importance (PI). SHAPPI generates stable and meaningful feature rankings, with immunization coverage and transit station activity identified as the most influential factors in all domains. These importance scores were subsequently embedded into the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to construct Pareto-optimal solutions. The optimization results demonstrate transparent trade-offs among health outcomes, economic fluctuations, and mobility changes, allowing policymakers to systematically evaluate competing priorities and design balanced intervention strategies. The findings confirm that the proposed framework successfully balances predictive performance, interpretability, and optimization, while providing a practical decision-support tool for epidemic management. Its generalizable design allows adaptation to diverse geographic and epidemiological contexts. In general, this research highlights the potential of hybrid machine learning and metaheuristic approaches to improve preparedness and policymaking in future health and socioeconomic crises.
Pengenalan Tulisan Tangan Aksara Bali Menggunakan Faster R-CNN Pratama, Alif Adwitiya; Sulistiyo, Mahmud Dwi; Ihsan, Aditya Firman
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5176

Abstract

In Balinese culture, the ability to read Balinese script is one of the challenges young generations face. Advances in machine learning have proposed handwriting detection systems using both traditional and deep learning models. However, the traditional approach is usually impractical and is prone to inaccurate identification results. Convolutional neural network (CNN)-based models integrate feature extraction and classification into an end-to-end pipeline to increase performance. This research proposes that recognizing characters through an object detection approach makes an end-to-end process of localizing and classifying several characters simultaneously using the Faster R-CNN. Four CNN models, including ResNet-50, ResNet-101, ResNet-152, and Inception ResNet V2, were tested to detect 28 Balinese characters in a single form that covers 18 consonants and 10 digits using Intersection over Union (IoU) thresholds: 0.5 and 0.75. ResNet-50 and Inception ResNet V2 achieve 0.991 mAP at IoU of 0.5, while Inception ResNet V2 excels at IoU of 0.75. Further analysis showed that the class ‘nol’ had the lowest Recall due to many undetected ground truths. Meanwhile, class ‘ba’ had the lowest Precision due to its similarity to classes “ga” and “nga”. This research contributes to the experiment with Faster R-CNN in detecting handwritten Balinese scripts.
Co-Authors Ade Romadhony Aditya Firman Ihsan Adiwijaya Agung Toto Wibowo Aji Gautama Putrada Akbar, M Raehan Alfian Akbar Gozali Andre Sitompul Azzam, Muhammad Abdullah Bayu Prabawa Bedy Purnama Bimmo Satryo Wicaksono Dawami, Hasbi Deny Haryadi Dewi Nasien Ema Rachmawati Ema Rachmawati Fachri Pane, Syafrial Faturahman, Farhan Febryanti Sthevanie Firdauz, Salma Salsabila Gamma Kosala Ganjar Gingin Tahyudin Gia Septiana Wulandari Hamdy Nur Saidy Hifzi Priabdi Jondri Jondri Marliani Harahap Muhammad Arzaki Muhammad Helmy Faishal Muhammad Rakha Muhammad Ridha Prasti Eko Yunanto Pratama, Alif Adwitiya Rasyid Kurniawan Rasyid Kurniawan Richasdy, Donny Rifqi Luthfil Hadi Rita Rismala Sabrina Adinda Sari Sailellah, Hassan Rizky Putra Sugondo Hadiyoso Syafrial Fachri Pane, Syafrial Fachri Tahyudin, Ganjar Gingin Tjokorda Agung Budi Wirayudha Tri Kusnandi Fazarudin Tri Kusnandi Fazarudin Tri Kusnandi Fazarudin, Tri Kusnandi Ubaidillah, Rifky Fahrizal Untari Novia Wisesty Wahyu Hauzan Rafi Wahyu Rismawan