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All Journal JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Sistem Komputer dan Informatika (JSON) Didaktik : Jurnal Ilmiah PGSD STKIP Subang Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Bulletin of Information System Research Jurnal Ilmu Komputer, Teknologi Dan Informasi Jurnal Manajemen, Pendidikan dan Ilmu Komputer (JMENDIKKOM)
Juni Ismail
Politeknik Bisnis Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences Other

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Implementasi Algoritma Fifo Terhadap Sistem Antrian Pasien di Rumah Sakit Berbasis Web Online Ismail, Juni; Gea, Muhammad Nasri; Satria, Habib; Tammamah Lubis, Hartati; Prasetya, Hardi; Hanani Hutabarat, Jamina; Sihombing, Rotua; Wanayumini, Wanayumini
JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING Vol. 7 No. 2 (2024): Journal of Electrical and System Control Engineering
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jesce.v7i2.10665

Abstract

The development of queuing systems in hospitals continues to be developed to optimally support patient service, especially regional general hospitals (RSUD). This is because the manual system is still inefficient, resulting in patients queuing for a long time and conflicts often occur. Based on these problems, a Web system was designed with the support of the Fifo algorithm so that the queuing system becomes simpler and more optimal. An easier and more flexible queuing system will support better and more excellent hospital services. Implementation of the Fifo Algorithm, designed to determine and calculate the patient queue system and orderly service for patient registration at the hospital. The implementation of this automatic queuing application will have an impact on health services, especially at Dr. Djasamen Saragih, Pematang Siantar city. The results of using a web-based application in this hospital have an impact in making it easier for operators or admins to queue up calls for patient serial numbers. The operating system on the website is monitored. If the web status has a data result of 1, the patient has been called by admin, but if the patient has not been called, the monitoring site is in Web with value 0 or null.
DEVELOPMENT OF SKIN CANCER PIGMENT IMAGE CLASSIFICATION USING A COMBINATION OF MOBILENETV2 AND CBAM Juni Ismail; Lili Tanti; Wanayumini Wanayumini
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6541

Abstract

Skin cancer is one of the most common types of cancer worldwide, making early detection a crucial factor in improving patient recovery rates. This study compares three classification methods for pigmented skin cancer images using a combination of VGG16 with CBAM, MobileNetV2 with CBAM, and a hybrid VGG16-MobileNetV2 approach with transfer learning. The dataset used in this study is the Skin Cancer ISIC - The International Skin Imaging Collaboration (HAM10000) from Kaggle, which consists of 10,015 images covering seven types of skin cancer. After balancing, the dataset was reduced to 2,400 images with three main classes: Actinic Keratosis (AKIEC), Basal Cell Carcinoma (BCC), and melanoma (MEL), each containing 800 images. This study involves data preprocessing stages such as augmentation, normalization, and image resizing to ensure optimal data quality. The model training process was conducted using the Adam optimizer, a batch size of 16, and an Early Stopping mechanism to prevent overfitting. Evaluation results indicate that the MobileNetV2 with CBAM model achieved the best performance with a validation accuracy of 86%, followed by the VGG16-MobileNetV2 combination at 77%, while VGG16 with CBAM experienced overfitting with an accuracy of 54%. Additionally, the best-performing model demonstrated a precision of 86.53% and a recall of 86.46%, highlighting its superior stability in detecting skin cancer compared to previous single-model approaches. With these results, the developed system can serve as an effective tool for medical professionals in performing early and more accurate skin cancer diagnoses
Evaluasi Performa Perceptron dan Adaline Pada Klasifikasi Angka Partisipasi Sekolah di Indonesia Nurhafizah Yazid; Juni Ismail
Jurnal Manajemen, Pendidikan Dan Ilmu Komputer Vol. 2 No. 2 (2025): Volume 2 No 2 Juli 2025
Publisher : Yayasan Darus Soleh Parung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65309/rtnta586

Abstract

Penelitian ini mengevaluasi performa Perceptron dan Adaline dalam mengklasifikasikan data Angka Partisipasi Sekolah (APS) di Indonesia. Data APS yang digunakan diperoleh dari Badan Pusat Statistik (BPS), mencakup 38 provinsi dan kelompok usia 16–18 tahun selama periode 2021–2024. Setelah melalui proses normalisasi, data dilatih menggunakan Perceptron yang mengandalkan fungsi aktivasi biner, serta Adaline yang memanfaatkan fungsi aktivasi linier dengan optimasi berbasis Mean Squared Error (MSE). Hasil pengujian menunjukkan bahwa Adaline mampu mencapai akurasi 86,84% dalam satu epoch, sementara Perceptron mencapai akurasi 84,21% setelah dua epoch. Temuan ini mengindikasikan bahwa Adaline lebih efisien dan lebih akurat dalam memproses data APS yang telah dinormalisasi. Penelitian ini memberikan kontribusi dalam pengembangan teknik klasifikasi data pendidikan, yang dapat dimanfaatkan sebagai dasar untuk perumusan kebijakan pendidikan yang lebih tepat sasaran.
“Perancangan Aplikasi Pariwisata Interaktif Kabupaten Simalungun Berbasis Multimedia Home Platform (MHP) pada Siaran Televisi Digital” Syaipuddin; Juni Ismail
Didaktik : Jurnal Ilmiah PGSD STKIP Subang Vol. 12 No. 02 (2026): Volume 12 No. 2, Juni 2026 Public
Publisher : STKIP Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36989/didaktik.v12i02.12297

Abstract

Television is an important electronic medium for delivering information and entertainment to the public. However, analog television broadcasting has limitations, particularly in terms of interactivity and multimedia content. With the development of digital broadcasting technology, television services can now provide interactive multimedia content for viewers. This study aims to develop interactive multimedia content for digital television using middleware, specifically the Icareus iTV Suite. The Icareus iTV Suite is used to develop interactive multimedia applications that can be accessed directly through home television. The results show that the developed application can enhance viewer interaction and provide various multimedia features that are easily accessible through digital television.
HYBRID TRANSFER LEARNING AND ADVANCED DATA AUGMENTATION FOR MULTICLASS BRAIN TUMOR CLASSIFICATION USING EFFICIENTNET A M H Pardede; Riki Winanjaya; Juni Ismail
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7524

Abstract

Accurate Accurate brain tumor diagnosis from MRI images remains challenging due to dataset limitations, class imbalance, and high morphological variability across tumor types. Existing deep learning approaches often yield suboptimal results when trained on small or imbalanced datasets. This study proposes a hybrid learning strategy that integrates transfer learning with advanced data augmentation to classify four brain tumor categories: glioma, meningioma, pituitary adenoma, and normal tissue. Using a large-scale dataset of 7,023 MRI images, the proposed framework incorporates Mixup, CutMix, and a comprehensive augmentation pipeline with an optimized EfficientNet-B0 architecture. The model achieves a test accuracy of 99.05% with F1-scores of 0.99, representing a 4.05 percentage point improvement over a baseline InceptionV3 model (95.00%) and outperforming ResNet-based approaches (93.80%) reported in previous studies. This quantitative improvement demonstrates the effectiveness of combining modern CNN architectures with advanced augmentation strategies. The streamlined architecture and high accuracy make the method suitable for deployment in resource-constrained healthcare environments. These results indicate that hybrid augmentation and transfer learning can deliver clinically meaningful performance for early brain tumor identification, offering a scalable and practical solution for computer-aided medical diagnosis
Komparasi Empat Kernel Support Vector Machine pada Klasifikasi Cyberbullying Twitter Berbahasa Indonesia Juni Ismail; Randi Sumitro
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 4 No 1 (2026): Januari 2026
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v4i1.265

Abstract

Cyberbullying on the Twitter social media platform has emerged as a significant social problem in Indonesia, with adverse effects on the mental health and well-being of its victims. Given the enormous volume of daily tweets, automated detection of cyberbullying expressions has become an urgent necessity. This study aims to compare the performance of four kernel functions in the Support Vector Machine (SVM) algorithm namely Linear, Radial Basis Function (RBF), Polynomial, and Sigmoid for cyberbullying classification on Indonesian-language tweets. The dataset used is a publicly available corpus of 13,169 annotated tweets released by Ibrohim and Budi in 2019. The preprocessing pipeline includes case folding, text cleaning, slang normalization using a colloquial dictionary, stopword removal, and stemming based on the Sastrawi library. Text features are extracted using Term Frequency–Inverse Document Frequency (TF-IDF) with a combination of unigrams and bigrams limited to the top 5,000 features. Model training is conducted on a stratified 80:20 split. Experimental results show that the RBF kernel achieves the highest performance with an accuracy of 0.8281 and an F1-score of 0.8269, slightly outperforming the Linear kernel (accuracy 0.8258; F1-score 0.8256). The Sigmoid kernel reaches an accuracy of 0.8204, while the Polynomial kernel records the lowest performance (accuracy 0.7674). The Linear kernel proves to be the most efficient option with the shortest training time (9.19 seconds) without significantly compromising accuracy. These findings can support the development of automated content moderation systems on Indonesian-language platforms.
Benchmarking CNN and Vision Transformer Architectures for Corn Leaf Disease Classification on the Kaggle Maize Dataset Juni Ismail; Raja Anan Nasution; Evi Handayani; Annisa Shafira Zuhri
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 4 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i4.9679

Abstract

Foliar diseases in corn pose a critical constraint on agricultural productivity, particularly in developing countries. Deep learning-based automated detection has emerged as a viable alternative to conventional manual inspection. This study presents a comparative evaluation of four contemporary deep learning architectures—EfficientNet-B3, MobileNetV3-Large, ResNet50, and Vision Transformer Small (ViT-Small)—on the publicly available Corn or Maize Leaf Disease Dataset hosted on Kaggle (4,188 image samples; four classes: Blight, Common Rust, Gray Leaf Spot, and Healthy). Class imbalance was addressed through a combination of WeightedRandomSampler and Focal Loss, while all architectures were trained via transfer learning from ImageNet pretrained weights, augmented with MixUp and CutMix. Experimental results demonstrate that ViT-Small achieved the highest classification performance, attaining 97.14% accuracy, a weighted F1-Score of 0.9716, and an AUC-ROC of 0.9961, outperforming EfficientNet-B3 (96.66%), MobileNetV3-Large (96.18%), and ResNet50 (95.71%). As an external reference, these results are also compared indicatively with the DenseNet121 accuracy (93.48%) reported by Waheed et al. (2020); it must be emphasized that this baseline was not reproduced in the present experiments, and therefore the comparison should be interpreted as indicative rather than conclusive. McNemar’s test confirmed that ViT-Small’s superiority is statistically significant (p<0.05). An ablation study verified the positive contribution of the Focal Loss and WeightedRandomSampler combination. Grad-CAM visualization corroborated that all models direct their attention to pathologically relevant lesion regions.
Mapping the Research Landscape of Multi-Objective Optimization by Ratio Analysis (MOORA) within Multi-Criteria Decision-Making: A Comprehensive Bibliometric and Science Mapping Analysis from 2012 to 2024 Juni Ismail; Alfry Aristo Jansen Sinlae; Zulfikar Zulfikar; Yanto Saputra; Elsy Rahajeng; Mesran Mesran
Bulletin of Information System Research Vol 3 No 2 (2025): April 2025
Publisher : Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/bios.v3i2.191

Abstract

The Multi-Objective Optimization by Ratio Analysis (MOORA) method has become an increasingly prominent technique within the multi-criteria decision-making (MCDM) family due to its computational simplicity, mathematical stability, and strong capacity to rank alternatives under conflicting criteria, yet the intellectual structure of this rapidly expanding field remains fragmented and insufficiently mapped. This study aims to systematically chart the global research landscape of MOORA within the MCDM domain and to identify its leading contributors, foundational works, dominant publication outlets, and prevailing thematic structures. A bibliometric research design guided by the PRISMA protocol was adopted, drawing on 275 English-language documents retrieved from the Scopus database for the period 2012 to 2024. The data were analysed using VOSviewer and Scopus analytical tools to examine annual publication trends, subject-area distribution, leading sources, co-citation networks, and keyword co-occurrence patterns. The results reveal a field that has accelerated sharply since 2018 and again after 2020, reaching a peak of seventy-one documents in 2024, with output concentrated in Engineering and Computer Science and disseminated through a heterogeneous ecosystem of mechanical-engineering, cleaner-production, and intelligent-systems outlets. Co-citation analysis confirms a theoretical base anchored in the canonical contributions of Brauers and Zavadskas, while keyword mapping shows MOORA functioning as a central decision-making nucleus closely tied to ratio analysis, optimisation, and surface-roughness applications, with TOPSIS, AHP, and WASPAS emerging as salient companion techniques. The novelty of this study lies in its focused mapping of the MOORA intersection rather than MCDM in general, exposing a loosely integrated thematic structure and a reliance on a narrow citation canon dominated by methodological pioneers. Its principal contribution is a consolidated knowledge map that clarifies the field's foundations and directs future methodological, fuzzy-extension, and interdisciplinary innovation
Evaluasi Leakage-Aware dan Imbalance-Sensitive pada BiLSTM dan Machine Learning Klasik untuk Klasifikasi Arah Pergerakan Harga Emas ANTAM Juni Ismail; Randi Sumitro; Juliana Rotua Pasaribu; Elida Madona Siburian; Renovand Mikael Situmorang
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 2 (2026): September 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i2.1346

Abstract

ANTAM gold is a widely used hedging instrument among Indonesian investors, yet determining the right moment to transact remains difficult because of its volatile and non-linear price movements. Several prior studies have reported near-perfect predictive accuracy; however, such results frequently stem from evaluation procedures that are prone to data leakage and therefore do not reflect genuine generalization ability. This study develops a leakage-aware and imbalance-sensitive evaluation framework for classifying the directional movement of ANTAM gold prices. Daily price data from 2010 to 2025 (5,751 samples) are transformed into 14 technical features—comprising lagged log-returns, volatility, momentum, moving-average ratios, and RSI—and labelled according to the sign of the five-day forward return. A Bidirectional Long Short-Term Memory (BiLSTM) model is benchmarked against Random Forest, Decision Tree, and a majority-class baseline using five-fold walk-forward validation with purging and train-only feature scaling. Performance is assessed through Balanced Accuracy, Macro-F1, the Matthews Correlation Coefficient (MCC), ROC-AUC, and PR-AUC. All classifiers outperform the majority baseline, with Decision Tree attaining the highest Macro-F1 of 0.534, followed by Random Forest (0.510) and BiLSTM (0.497), and a best MCC of 0.074. These findings indicate limited but real directional predictability and confirm that rigorous evaluation yields markedly more conservative and credible performance estimates than the inflated accuracies claimed in earlier work.
Analisis Komparatif YOLO11 dan RT-DETR untuk Deteksi Sampah pada Variasi Pencahayaan Juni Ismail; Pangidoan Adventus Ambarita; Renovand Mikael Situmorang; Elida Madona Siburian; Inggrid Ester Erlinda Simarmata
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 2 (2026): September 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i2.1358

Abstract

Sistem pemilahan sampah berbasis citra memerlukan model deteksi objek yang mampu mempertahankan akurasi ketika kualitas visual berubah. Penelitian ini menganalisis ketahanan visual YOLO11n, YOLO11s, dan RT-DETR-l pada enam kelas objek sampah, yaitu biodegradable, cardboard, glass, metal, paper, dan plastic. Data eksperimen terdiri atas 7.324 citra latih, 2.098 citra validasi, dan 1.042 citra uji dengan anotasi bounding box berformat YOLO. Evaluasi dilakukan pada test set normal dan empat skenario gangguan, yaitu pencahayaan redup 50%, pencahayaan terang 50%, kontras rendah, dan Gaussian noise. Metrik evaluasi meliputi precision, recall, F1-score, mAP@50, mAP@50:95, dan estimasi FPS. Hasil pengujian normal menunjukkan bahwa RT-DETR-l memperoleh performa tertinggi dengan precision 0,5329, mAP@50 0,4484, dan mAP@50:95 0,3576. Pada evaluasi robustness, RT-DETR-l tetap paling stabil, khususnya pada skenario redup 50% dengan mAP@50 0,4463. Sebaliknya, YOLO11n menghasilkan efisiensi inferensi tertinggi dengan 178,32 FPS pada kondisi normal. Temuan ini menegaskan adanya trade-off antara akurasi deteksi, ketahanan visual, dan kecepatan inferensi untuk sistem deteksi sampah real-time.
Co-Authors Alfry Aristo Jansen Sinlae Annisa Shafira Zuhri Elida Madona Siburian Elsy Rahajeng Evi Handayani Gea, Muhammad Nasri Habib Satria Hanani Hutabarat, Jamina Inggrid Ester Erlinda Simarmata Juliana Rotua Pasaribu Lili Tanti Mesran Mesran Nurhafizah Yazid Pangidoan Adventus Ambarita Pardede, Akim Manaor Hara Prasetya, Hardi Raja Anan Nasution Randi Sumitro Renovand Mikael Situmorang Riki Winanjaya Sihombing, Rotua Syaipuddin Tammamah Lubis, Hartati Wanayumini Yanto Saputra Zulfikar Zulfikar