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All Journal Proceedings of KNASTIK Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Teknologi Informasi dan Ilmu Komputer Telematika JUITA : Jurnal Informatika Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Jurnal Eksplora Informatika Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia Jurnal Pengabdian Masyarakat Indonesia JUSTIN (Jurnal Sistem dan Teknologi Informasi) Journal of Computing Theories and Applications Pamasa: Jurnal Pengabdian kepada Masyarakat Journal of Electronics Technology Exploration (JOeTEx) Mengabdi: Jurnal Hasil Kegiatan Bersama Masyarakat Journal of Industrial and Mechanical Engineering
Lasmedi Afuan
Informatika, Fakultas Teknik, Universitas Jenderal Soedirman, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences Other

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PATTERN CLASSIFICATION SIGN LANGUAGE USING FEATURES DESCRIPTORS AND MACHINE LEARNING Nurhadi, Nurhadi; Winanto, Eko Arip; Said, Rahaini Mohd; Jasmir, Jasmir; Afuan, Lasmedi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1228

Abstract

Sign language is way of communication for the deaf and speech impaired. In Indonesia, the utilization of a standardized language involves the incorporation of American Sign Language (ASL). ASL is employed for various communication needs, ranging from basic alphanumeric fingerspelling (A-Z and numbers) to the more complex SIBI form (comprising gesture vocabulary) in everyday interactions as well as formal contexts. This surge in the digitization of sign language underscores the ongoing advancements in research and development. The challenge in this research lies in the ability to recognize American Sign Language (ASL) with diverse intensities and invariant backgrounds. Therefore, the study emphasis is on proposing a suitable segmentation method comparison for multi-intensity ASL cases. Subsequently, global feature descriptor methods, including Color Histogram, Hu Moments, and Haralick Texture techniques, are applied for feature extraction. The result of the Logistic Regression method versus the supervised Random Forest checks accuracy and suitability in identifying ASL fingerspelling. The findings of this research is predictive value of logistic regression is 48%, with class Y having the highest precision (0.86), class V having the lowest accuracy (0.16), and class L having the highest recall (0.73). The maximum precision in classes B, F, H, I, K, Y, and Z is 1.00, and the lowest in class U is 0.58, while the highest recall is in class G, which is 1.00. The lowest is in class V, while the predictive value from the random forest is 86 percent. Class H has the greatest f1 score (0.99), while class U has the lowest f1 score (0.64). The Random Forest method outperforms the two methods suggested in the paper, according to the comparison.
THE EFFECT OF UNIGRAM AND BIGRAM IN THE NAÏVE BAYES MULTINOMIAL FOR ANALYZING OF COMMENT SENTIMENT OF GOJEK APPLICATION IN GOOGLE PLAY STORE Adyatma, Adrian Dwinanda; Afuan, Lasmedi; Maryanto, Eddy
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.1310

Abstract

In sentiment classification systems that use Naïve Bayes Classifier, a commonly used feature extraction method is TF-IDF with unigram and bigram, where the two is used separately. In the reality, most of texts contain single or composed word,so it is needed to use the combination of unigram and bigram to maximize the accuracy of the classification results. In this research, the impact and performance improvement between classification systems using unigram or bigram solely and those using a combination of both are studied. Using 1000 data of reviews with ratings 1 (negative) and 5 (positive) from Gojek users on the Google Play Store, and performing performance validation with K-Fold at K=10, the system that uses the combined TF-IDF feature extraction of unigrams and bigrams achieves the best performance among the three systems with an accuracy of 0.84, however the accuracy of the system that uses unigrams solely has accuracy of 0.83, and 0.7 for the system that uses bigram. From the results of the research, it can be concluded that the use of the combination of unigram and bigram can increase the accuracy of the classification result.
Milkfish Freshness Detection Based On Eye Images Using Convolutional Neural Network (CNN) With Mobilenetv3 Architecture On A Mobile Application Musaadah, Khalimah; Afuan, Lasmedi; Permadi, Ipung
Journal of Electronics Technology Exploration Vol. 3 No. 2 (2025): December 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joetex.v3i2.649

Abstract

Indonesia has abundant fishery resources, making it one of the world's largest producers and consumers of fish. One of the most commonly consumed types is milkfish (Chanos chanos). Before consumption, it is important to determine the freshness level of the fish. This freshness can be identified using a Convolutional Neural Network (CNN) model with the MobileNetV3 architecture, which is efficient and suitable for mobile application implementation. This study aims to detect the freshness level of milkfish based on eye images using the MobileNetV3 CNN architecture implemented in a mobile application. The dataset used consists of 500 images, divided into training, validation, and testing sets with proportions of 70%, 20%, and 10%, respectively. The data underwent preprocessing, including resizing and image augmentation, to increase data variation. The model was developed using hyperparameter tuning with both random search and grid search methods. The results show that random search achieved better performance with a training accuracy of 92.88%, validation accuracy of 89.90%, and an overall test accuracy of 91%. The trained model was successfully implemented into a mobile application named ScanBang, which can classify the freshness level of milkfish and display its confidence score in a practical and user-friendly manner.
Prescriptive Learning Analytics for Student Dropout: Integrating Temporal Velocity and Counterfactual Explanations in Longitudinal Data Nurul Hidayat; Lasmedi Afuan; Helmi Roichatul Jannah
Journal of Computing Theories and Applications Vol. 3 No. 4 (2026): JCTA 3(4) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15920

Abstract

Student dropout in higher education remains a persistent socioeconomic challenge, yet many predictive models reported in the literature are methodologically compromised by randomized cross-validation schemes that introduce temporal data leakage and artificially inflate predictive performance. This study proposes a longitudinal prescriptive learning analytics framework integrating three complementary methodological components: a Leave-One-Cohort-Out (LOCO) temporal validation protocol, a hybrid SMOTE-ENN class balancing strategy, and temporal velocity feature engineering derived from Learning Management System (LMS) behavioral trajectories. The framework was evaluated on a longitudinal dataset comprising 464,739 enrollment records and 77 features. Five predictive algorithms—XGBoost, LightGBM, CatBoost, Random Forest, and Logistic Regression—were comparatively assessed on a strictly isolated blind holdout cohort (2022), with CatBoost emerging as the champion estimator, achieving a PR-AUC of 0.8859, a Macro F1-Score of 0.9143, and the lowest Brier Score (0.0221), thereby demonstrating superior calibration and discriminative capability under severe class imbalance (93:7 ratio). Comprehensive ablation analysis revealed that temporal velocity features function not merely as additive predictors, but as a structural prerequisite enabling Synthetic Minority Oversampling Technique with Edited Nearest Neighbors (SMOTE-ENN) to generate high-quality synthetic boundary instances; removing these features reduced minority-class precision from 0.8302 to 0.6721. To operationalize predictive outputs into actionable intervention pathways, Diverse Counterfactual Explanations (DiCE) were implemented under a three-tier causal constraint architecture on 96 borderline high-risk students, generating 384 feasible intervention scenarios exclusively targeting forward-looking behavioral velocity metrics without constraint violations. Collectively, these findings advance the paradigm of prescriptive learning analytics by providing educational institutions with interpretable risk diagnostics and operationally feasible intervention guidance grounded in empirically validated behavioral and temporal dynamics.
Pemilihan Lokasi Terbaik Pemasangan Billboard Untuk Media Promosi Program Studi Menggunakan Metode ELECTRE II Pandu Wahyu Aji; Katon Muhammad; Zakiyyan Alkaf; Lasmedi Afuan; Nurul Hidayat
Journal of Industrial and Mechanical Engineering Vol 3 No 1 (2025): Journal of Industrial and Mechanical Engineering
Publisher : Department of Industrial Engineering, Universitas Jenderal Soedirman.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jimien.2025.3.1.14600

Abstract

Dalam upaya meningkatkan visibilitas dan daya tarik Program Studi Teknik Industri Unsoed, pemilihan lokasi pemasangan billboard menjadi aspek penting dalam strategi promosi. Lokasi yang strategis perlu dianalisas secara objektif dengan mempertimbangkan beberapa kriteria, seperti biaya, ukuran billboard, kepadatan lalu lintas, durasi lampu lalu lintas, dan jarak ke pusat kota. Pemilihan ini bertujuan menentukan lokasi terbaik untuk pemasangan billboard promosi di wilayah Purwokerto menggunakan metode Electre II. Metode ini melibatkan normaliasai data, pembobotan kriteria, serta analisis nilai concordance dan discordance untuk memperoleh peringkat alternatif lokasi. Hasil analisis menunjukan bahwa Simpang 4 Srimaya merupakan lokasi paling optimal dengan skor tertinggi, ditunjang kepadatan lalu lintas tinggi (150 kendaraan per menit) dan rata-rata durasi lampu lalu lintas yang lama (125 detik). Lokasi potensial lainnya adalah simpanng 4 Klenteng SKJ dan Simpang 4 GOR Satria. Temuan ini memberikan rekomendasi strategis dalam pemilihan lokasi iklan luar ruang yang efektif dan eisien untuk promosi program studi kepada calon mahasiswa.
Transformer-Based Multi-Class Intrusion Detection Using CICIoMT2024 Dataset for Secure IoMT Networks Winanto, Eko Arip; Sharipuddin, Sharipuddin; Purnama, Benni; Nurhadi, Nurhadi; Afuan, Lasmedi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5512

Abstract

Internet of Medical Things (IoMT) ecosystems significantly enhance healthcare services but simultaneously expand the attack surface, exposing medical networks to diverse cyber threats such as distributed denial-of-service and spoofing attacks. Existing intrusion detection systems for IoMT are often limited to binary classification and struggle to capture complex multi-class attack behaviors, particularly under highly imbalanced data distributions. This study proposes a deep Transformer-based intrusion detection model as a reproducible baseline for multi-class intrusion detection in IoMT environments. The model is evaluated on the CICIoMT2024 dataset, which comprises 19 traffic classes including benign and multiple attack categories. Data preprocessing involves stratified data splitting, feature normalization, and label encoding to ensure fair evaluation. The proposed baseline employs a six-layer Transformer encoder with eight attention heads and is trained using the AdamW optimizer. Experimental results demonstrate an overall accuracy of 98.76% and a macro F1-score of 0.92, indicating strong detection capability across most attack classes. The model achieves excellent performance on benign traffic and high-volume attacks such as DDoS and DoS, while performance degradation is observed on minority classes, including ARP spoofing, highlighting the impact of class imbalance. These findings establish the proposed Transformer model as a transparent and robust baseline for IoMT intrusion detection research. By providing reproducible performance benchmarks, this work supports future development of hybrid and imbalance-aware detection mechanisms aimed at enhancing real-time security in medical cyber-physical systems.
Co-Authors Abidin, Dodo Zaenal Adi Pangestu Adyatma, Adrian Dwinanda Afrizal Nehemia Toscany Ahmad Ashari Ahmad Fauzi Ridlwan Alfarez Marchelian, Reyno Andreas, Roy Anin Ammbya Soulani Arief Kelik Arief Kelik Nugroho Arief Kelik Nugroho Arief Kelik Nugroho Arief Kelik Nugroho As'ad, Mohamad Faris Asmoro Widagdo, Asmoro Bangun Wijayanto Bintang Pradana Yosua, Panky Dadang Iskandar Dadang Iskandar Daffa Ammar Muaafii Daffa Naufaldi Al Rasyid Didit Suprihanto, Didit Dodi Sandra Eddy Maryanto Eddy Maryanto Fandy Setyo Utomo Faris Akbar Abimanyu Febri Sutomo Ferry Darmawan Helmi Roichatul Jannah Hidayat, Nurul Indah Cahya Febriani Indyastuti, Devani Laksmi Ipung Permadi Ipung Permadi Ipung Permadi Ipung Permadi Iqbal Iqbal Irfan Agus Tiawan Jasmir, Jasmir Joe, Michael Katon Muhammad Khanza, Muthia Kharisun, Kharisun Kurniawan, Yogiek Indra Maria Ulfa Chasanah Muhammad Fikri Rivaldi Muhammad Luthfi Muhammad Randy Cahya Mardika Muhammad Zein Albalki Musaadah, Khalimah Najmudin Nandha Arwiansyah Nasichatul Umayah Nofiyati Nofiyati, Nofiyati Nofiyati, Nofiyati Nur Chasanah Nurhadi Nurul Hidayat Nurul Hidayat Nurul Hidayat Nurul Ismailiah Pandu Wahyu Aji Priandika Ratmadani Anugrah Purnama, Benni R. Rizal Isnanto Rahayu, Swahesti Puspita Rif’an, Muhammad Rista Afifah Rochmat Mulyo Sugihono Said, Rahaini Mohd Sari, Enjelita Sharipuddin, Sharipuddin Siti Nurhayati SRI LESTARI Susi Setianingsih Teguh Cahyono Tuti Alawiyah Victoria Angela Sugianto Wahid, Arif Mu'amar Yohanes Suyanto Yunindar, Galih Arditiya Zahira Hasyati, Adila Zakiyyan Alkaf