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All Journal Jurnal Ilmu Komputer dan Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi POROS TEKNIK Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Antivirus : Jurnal Ilmiah Teknik Informatika JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Journal of Applied Data Sciences Yayasan Cita Cendikiawan Al Khwarizmi Jumat Pendidikan: Jurnal Pengabdian Masyarakat JUSTIN (Jurnal Sistem dan Teknologi Informasi) Kontribusi: Jurnal Penelitian dan Pengabdian Kepada Masyarakat Algoritme Jurnal Mahasiswa Teknik Informatika PELS (Procedia of Engineering and Life Science) IJCOSIN : Indonesian Journal of Community Service and Innovation JOMPA ABDI: Jurnal Pengabdian Masyarakat eProceedings of Engineering Nusantara Journal of Computers and its Applications JOSAR (Journal of Students Academic Research) Inovasi Jurnal Pengabdian Masyarakat (IJPM) Jurnal Informatika Polinema (JIP) Pengabdian Kepada Masyarakat Bidang Teknologi dan Sistem Informasi Scientific Journal of Informatics Jnanadharma Jurnal Kendali Teknik dan Sains Journal of Software Engineering and Multimedia (JASMED)
Dimas Fanny Hebrasianto Permadi
Telkom University
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Inovasi IoT Biopon BSF Untuk Pengelolaan Sampah Di Unit BUMDes TPST 3R Sokaraja Hebrasianto Permadi, Dimas Fanny; Utami, Annisaa; Dwi Pratiwi, Evia Zunita; Hapiz, Ervan; Hakim Putra Antara, Wildan Daffa’; Dharma Putra, Adhitana
Jompa Abdi: Jurnal Pengabdian Masyarakat Vol. 4 No. 4 (2025): Jompa Abdi: Jurnal Pengabdian Masyarakat
Publisher : Yayasan Jompa Research and Development

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57218/jompaabdi.v4i4.2178

Abstract

Pengelolaan sampah organik di Unit BUMDes TPST 3R Sokaraja masih menghadapi kendala dalam efisiensi dan pemantauan. Black Soldier Fly (BSF) mampu menguraikan sampah organik dengan cepat sekaligus menghasilkan pupuk organik (kasgot) dan pakan ternak bernilai ekonomis. Pengabdian masyarakat ini bertujuan mengoptimalkan pengelolaan sampah melalui biopon BSF berbasis Internet of Things (IoT) untuk monitoring real-time kondisi biopon, termasuk suhu, kelembaban, dan volume sampah. Metode yang diterapkan meliputi instalasi biopon, pemasangan sensor IoT, pelatihan pengurus BUMDes, dan evaluasi pengurangan sampah serta kualitas produk sampingan. Hasil menunjukkan sistem ini mempermudah pemantauan, mempercepat penguraian sampah organik, dan menghasilkan kasgot serta pakan ternak berkualitas. Inovasi ini mendukung pengurangan sampah organik sekaligus memberikan nilai ekonomi tambahan bagi masyarakat desa
IMPLEMENTATION OF ANIMAL FACE’S RECOGNITION BY CONVOLUTIONAL NEURAL NETWORK (CNN) ALGORITHM Dimas Fanny Hebrasianto Permadi; Moch Zawaruddin Abdullah
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 21, No. 1, January 2023
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v21i1.a1131

Abstract

The purpose of this research is to apply the Convolutional Neural Network (CNN) method in the field of Computer Vision. The CNN algorithm is a combination of Neural Network and Multilayer Perceptron which uses a convolution approach to extract features. The CNN technique was used to identify an animal dataset that has 16,130 images divided into three categories: Cats, Dogs and Wild. This study aims to recognize facial images of animals belonging to the category of Cats, Dogs or Wild Animals which resemble the derivatives of cats or dogs such as Lions, Tigers, Hyenas, Wolves and so on. Comparing to learning rate and epoch, the results are 10e-4 and 60 respectively. Utilizing random images from the datasets, learning rate and epoch may achieve an accuracy of about 97.22% or 116.33 out of 120 images. When using images taken outside of the datasets, the accuracy may be as high as 77.78% or 93.33 out of 120 images.
Perancangan Sistem Pemantauan Kelelahan Driver Berbasis IoT (Internet Of Things) Yang Adaptif Untuk Transportasi Makanan Segar: Studi Kasus Di Industri Logistik Miftahol Arifin; Dimas Fanny Hebrasianto Permadi Kasanah; Pradana Ananda Raharja; Nabila Noor Qisthani; Fikra Titan Syifa; Faizah Faizah
Jurnal Kendali Teknik dan Sains Vol. 1 No. 4 (2023): Oktober: Jurnal Kendali Teknik dan Sains
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jkts-widyakarya.v1i4.2959

Abstract

In the logistics industry, fresh food transportation requires effective management of driver fatigue to maintain safety and service quality. With the increasing digital connectivity, Internet of Things (IoT) technology has become a promising solution to monitor driver fatigue in real-time. This research aims to design an adaptive IoT-based driver fatigue monitoring system specifically for fresh food transportation. Through a case study in the logistics industry, we identified specific challenges and needs in driver fatigue management. Based on the identification results, we developed a system that utilizes IoT sensors integrated in vehicles and drivers' smartphones. The system automatically monitors the driver's physical condition and behavior and provides real-time alerts if signs of fatigue are detected. With an adaptive approach, the system can customize recommendations and interventions according to the measured fatigue level. The results of the case study show that the designed system is able to reduce the risk of driver fatigue, increase transportation safety, and improve service quality in fresh food transportation. This research contributes to the practical understanding of the implementation of IoT technology in driver fatigue management, particularly in the context of the logistics and fresh food transportation industries.
Dota 2 Hero Buff And Nerf Predictions Based On Professional Match Data Using Random Forest Muhammad Raditya Azanata; Muhamad Azrino Gustalika; Dimas Fanny Hebrasianto Permadi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2698

Abstract

Balancing updates (buffs and nerfs) are essential in Multiplayer Online Battle Arena games because minor parameter changes can shift the competitive metagame and reduce hero diversity. Unlike previous studies on Dota 2 that focus on match outcome prediction, this study introduces and evaluates hero-centric balance recommendations against official patch actions across patch transitions. To address this gap, this work contributes a patch-to-patch (Version 7.39-7.40b) external validation protocol that compares recommendations from patch t with developer actions in patch t+1 using patch notes. Professional match records were gathered from public sources and sorted by hero and patch into combat, economic, and impact categories. This study proposed a data-driven pipeline to classify each Dota 2 hero as overpowered, underpowered, or balanced from professional match telemetry and to translate these classes into balance recommendations (Nerf, Buff, or Balance). Labels derive from win-rate and pick-rate distributions using statistical control limits (μ ± kσ, k = 0.3) to ensure transparent, repeatable labeling. A Random Forest classifier was trained using grid-searched hyperparameters and evaluated using stratified 6-fold cross-validation with macro-averaged F1 to address class imbalance. Internal evaluation achieved 0.94 accuracy and 0.84 macro-F1. For external validation, patch t recommendations were compared to official balance actions in patch t+1 during six successive transitions; accuracy ranged from 0.436 to 0.672 (mean 0.559), with the best result on 7.39b to 7.39c (84/125). These results indicated that professional telemetry could support interpretable balance monitoring and provide early signals for buff/nerf candidate review.
Development and Validation of a 2D Educational Adventure Game on Dental Health Using the GDLC Method Novandi Hidayat Hidayat; Dimas Fanny Hebrasianto Permadi
Journal of Software Engineering and Multimedia (JASMED) Vol. 3 No. 2 (2025): Journal of Software Engineering and Multimedia (JASMED)
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/jasmed.v3i2.9528

Abstract

Currently, dental and oral health problems in Indonesia are still quite high, because according to the 2018 Basic Health Research Results, the largest proportion of dental problems in Indonesia are damaged/cavities/painful teeth. Oral diseases can cause pain, suffering, psychological obstacles, and social deprivation, which are very detrimental to both individuals and citizens, including children. Poor oral conditions, such as many cavities, can disrupt daily activities. The purpose of this research is that games created using the Game Development Lifecycle (GDLC) method can convey information to users, and when played by players, the gameplay stage will be examined using the Blackbox Testing method. The creation of an educational adventure game project plays an important role in increasing insight by utilizing technology to understand the science of dental and oral diseases. The party involved in this research is a dental expert to validate this game according to valid material. With this research, results can be obtained with a validity value on the educational game material and a feasibility value. Later, the percentage value will determine whether this game is good or bad, using the Game Development Lifecycle (GDLC) methodology. Of the two dentists who have validated the material in the Denterra educational game, the average result of expert validation reached 91% valid, meaning the material in this educational game is Very Feasible according to the Achievement Level Conversion Table. The results of Blackbox Testing reached a value of 94% or 237/252, with features that are less agree that the feature is functional for players are the "Level" button (Before completing the game), "Dental Plaque beats PD", and "Dental Crust beats PD". From the Blackbox Testing Analysis, it can be concluded that the functionality of the Denterra educational game is Very Feasible for use by the public. Then, the difficulty level at each level is appropriate for those with 3 players. The sample data that has been produced is not comparable to the student population in Banyumas Regency, due to time constraints and the environmental conditions of the subjects.
Comparative Analysis on Data Balancing and Augmentation in Skin Cancer Image Classification Using Multiple Datasets with Explainable AI Dimas Fanny Hebrasianto Permadi; Annisaa Utami; Muhammad Raafi’u Firmansyah
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 2 (2026): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v19i2.1598

Abstract

Skin cancer is one of the most common types of cancer worldwide, with a continuously increasing incidence rate. Early detection and accurate classification of skin lesions are crucial for improving patient survival, particularly for melanoma. This study presents a comparative analysis of data-balancing strategies for skin cancer image classification across multiple publicly available datasets, including ISIC 2024, ISIC 2019, ISIC 2020, HAM10000, and PROVe-AI. The ConvNeXt-Tiny architecture is employed as the classification model and evaluated under three dataset configurations, namely RAW without balancing, Undersampling, and Oversampling via data augmentation, combined with two Learning Rate (LR) settings of 0.001 and 0.0001. In addition to reporting classification performance, this study emphasizes a comparative evaluation of accuracy, training stability, and computational efficiency across different balancing strategies. The experimental results indicate that the RAW dataset with an LR=0.0001 provides the best trade-off between performance and efficiency, achieving a validation accuracy of 99.88% and an F1-score of 0.9988. Oversampling via augmentation achieves the highest performance, with a validation accuracy of 99.93% and an F1-score of 0.9993, but requires substantially higher computational resources. Undersampling enables faster training with lower resource consumption, although it results in a slight performance degradation. Furthermore, Explainable Artificial Intelligence techniques, including Grad-CAM and LIME, demonstrate that models trained with an appropriate learning rate consistently focus on clinically relevant lesion regions, thereby improving interpretability and trustworthiness.
Logistic Regression Analysis of Factors that Influence User Experience in Student Medical Report Applications Tenia Wahyuningrum; Novian Adi Prasetyo; Gita Fadila Fitriana; Dimas Fanny Hebrasianto Permadi; Rr. Setyawati; Joewandewa Yuliansyah; Khoem Sambath
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.285

Abstract

Monitoring student health efficiently requires collaboration between schools and government health services. Traditional methods often need more agility and user-friendliness, leading to delays and inaccuracies. This research aims to verify a fast and agile student medical report that we have previously developed using the Modified Agile User Experience (UX) method, with a focus on simplicity, usability, and accessibility. The system’s evaluation employs non-functional testing methods to identify factors influencing user satisfaction within the scope of the user experience. We measure task-level and overall user satisfaction using the Single Ease Questions (SEQ) questionnaire as the response variable. This study also investigates test-level satisfaction as predictor variables using Usability Metric for User Experience (UMUX) and UMUX-Lite questionnaire as predictor variables, as well as each student’s Interest in learning and learning motivation concerning test-level satisfaction. Binary Logistic Regression (BLR) analysis determined the relationship between test-level and task-level satisfaction, revealing significant correlations between these variables. Based on the results, the Interest to Learn variable is the most important factor that influences task-level satisfaction, but with a small probability value (42.9%). To ensure these accurate results, we changed the scale on SEQ from Easy and Hard to seven scales with normalized values. We compared the results using 4 algorithms: Logistic Regression, Random Forest, Support Vector Machine (SVM), and Gradient Boosting as the most effective model. For a test size of 0.2 and a random state of 40, Logistic Regression achieved an accuracy of 0.80 and a Receiver Operating Characteristics (ROC) and Area Under Curve (AUC) score of 0.83. Random Forest also had an accuracy of 0.80 but a slightly lower ROC AUC score of 0.77. SVM also performed well, with accuracies of 0.83 and ROC AUC scores of 0.77. Gradient Boosting showed the lowest performance with an accuracy of 0.77 and a ROC AUC score of 0.73. These results indicate that Logistic Regression is the most robust model for predicting user satisfaction. Significant data correlations between SEQ, UMUX, and UMUX-Lite guide the development of user-centered applications, enhancing the effectiveness of educational tools by ensuring higher user satisfaction. Future research should consider more extensive, more diverse samples and additional factors influencing user experience to refine these models and their applications.
Co-Authors Abednego Dwi Septiadi Agus Zainal Arifin Alon Jala Tirta Segara Alwi, Muhammad Nazar Amelia Sahira Rahma Anggi Zafia Anggita Mawar Sari Annisaa Utami Ari Setyo Ashari, Rahmat Asrozy, Muhammad Faisal Aulia Desy Nur Utomo Belqis Nur Ivada Navalufi Bita Parga Zen Budi Prasetyo Cahyo Aji Pangestu Chevin, Virginawan Alessandro David Saputra, Ricko Dedy Agung Prabowo Dharma Putra, Adhitana Dian Prawesty, Maharani Dini Adni Navastara, Dini Adni Eka Tripustikasari Elisa Dwi Okta Sari Endang Sri Rahayu Evia Zunita Dwi Pratiwi Fahrur Riska Ulinnuha Faizah Faizah Fikra Titan Syifa Gita Fadila Fitriana Hakim Putra Antara, Wildan Daffa’ Hapiz, Ervan Hartami Santi, Indyah Henri Tantyoko Hidayatuloh, Muchamad Azis Imam Wahyudi Ivanda Vipriyandhito Joewandewa Yuliansyah Jumanto Unjung Kartika, Kurnia Paranita Khoem Sambath Khoirul Mujib Kurnia Paranita Kurniawan, Dika Dwi Lestanti, Sri Melinda Sukma Ayu Adien Arera Miftahol Arifin Moch Zawaruddin Abdullah Moh Aris Triprastya Muhamad Awiet Wiedanto Prasetyo Muhamad Azrino Gustalika Muhamad Azrino Gustalika Muhammad Raafi’u Firmansyah Muhammad Raditya Azanata Mukarromah, Firma Nabila Noor Qisthani Nadiyah Rahayu Nicolaus Euclides Wahyu Nugroho Novandi Hidayat Hidayat Novian Adi Prasetyo Novian Adi Prasetyo Pandu Kusuma, Abdi Pradana Ananda Raharja Priadi Priadi Rahayu, Nadiyah Rahma Soraya, Anisha Rahma, Amelia Sahira Riyonaldi Dwia Juang Saputra Rizka Wakhidatus Sholikah Rofiatul Muzayyanah Rr. Setyawati Sa'adah, Aminatus Santi, Indyah Hartami Sri Lestari Tenia Wahyuningrum Utami, Annisaa Vipriyandhito, Ivanda Vit Zuraida Wahyu Setiawan Yogi Prasetyo Yolanda, Nedya Yordan Setyawan Yoso Adi Setyoko Yuana, Haris Yulia, Devinda