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JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Published by STMIK Nusa Mandiri
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Computer Science & IT
Kegiatan menonton film merupakan salah satu cara sederhana untuk menghibur diri dari rasa gundah gulana ataupun melepas rasa lelah setelah melakukan aktivitas sehari-hari. Akan tetapi, karena berbagai alasan terkadang seseorang tidak ada waktu untuk menonton film di bioskop. Dengan bantuan media internet, berbagai macam aplikasi nonton film android sangat mudah dicari. Hanya bermodalkan smartphone saja para penonton film dapat streaming berbagai macam jenis film di mana saja dan kapan saja mereka inginkan. Akan tetapi, karena banyaknya pilihan aplikasi nonton film android yang bisa digunakan, terkadang seseorang bingung memilihnya. Untuk itu, diperlukan suatu sistem pendukung keputusan yang dapat digunakan para pengguna sebagai alat bantu pengambilan keputusan untuk memilih dengan berbagai macam kriteria yang ada. Salah satu metode yang digunakan adalah metode Analytical Hierarchy Process (AHP). AHP melakukan perankingan dengan melalui penjumlahan antara vector bobot dengan matrik keputusan dengan tujuan agar hasil yang diberikan lebih baik dalam menentukan alternatif yang akan dipilih. Berdasarkan hasil penelitian yang dilakukan oleh 36 sampel responden didapatkan kriteria konten menjadi prioritas pertama pengguna untuk memilih aplikasi nonton film android dengan nilai bobot sebesar 0,224. Sedangkan Netflix menjadi alternatif dengan prioritas pertama keputusan pengguna dalam memilih aplikasi nonton film android dengan nilai bobot sebesar 0,352.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 465 Documents
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ENHANCING COFFEE PRODUCTION FACTOR ASSESSMENT USING LINEAR REGRESSION AND AHP FOR RELIABLE WEIGHT CONSISTENCY Aris Gunaryati; Teddy Mantoro; Septi Andryana; Benrahman; Mohammad Iwan Wahyuddin
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6788

Abstract

The agricultural sector, particularly coffee production, plays a crucial role in Indonesia’s economy as both a domestic commodity and an export product. However, efforts to optimize coffee production are often constrained by traditional Multi-Criteria Decision-Making (MCDM) methods that rely heavily on subjective judgments, leading to potential inconsistencies—especially in the presence of multicollinearity among variables. This study addresses that challenge by proposing a data-driven and consistent weighting method that integrates Multiple Linear Regression (MLR) with the Analytic Hierarchy Process (AHP). Regression coefficients derived from MLR—based on variables such as the area of immature (-0.2419), mature (0.8357), and damaged (0.5119) coffee plantations—are normalized and incorporated into the AHP pairwise comparison matrix. The resulting Consistency Ratio (CR) values are all below 0.1, indicating high internal consistency and statistical reliability of the derived weights. This integrated approach offers an objective and systematic foundation for MCDM in coffee production analysis, enhances the accuracy of agricultural planning, and supports evidence-based policymaking, while also providing a replicable model for addressing similar challenges in other sectors
OPTIMIZING DECISION TREE PERFORMANCE WITH RECURSIVE FEATURE ELIMINATION FOR HIGH-DIMENSIONAL MUSHROOM CLASSIFICATION Tanti, Lili; Safrizal; Thanri, Yan Yang
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6816

Abstract

Classifying mushroom species presents a significant challenge within biological data analysis because of the wide variety of species and their distinct attributes. This research investigates the effectiveness of the Decision Tree classifier for mushroom categorization by comparing two splitting criteria, the Gini Index and Entropy. Additionally, the study employs the Recursive Feature Elimination (RFE) method for dimensionality reduction to enhance model efficiency and performance. The dataset was collected, cleaned, and analyzed exploratorily before feature selection was conducted using RFE. The Decision Tree model was trained and evaluated using accuracy, precision, recall, and F1-score metrics. The results showed that applying RFE improved computational efficiency without compromising model accuracy. The Gini criterion provided more stable results across all metrics, while Entropy demonstrated higher precision in certain cases. Model optimization through parameter tuning produced the best parameter combination at max_depth = 5, min_samples_leaf = 5, and min_samples_split = 10. This study concludes that integrating RFE with the Decision Tree can significantly enhance the performance of high-dimensional dataset classification. The findings are expected to serve as a reference for developing efficient and accurate biological data classification models
SYSTEMATIC LITERATURE REVIEW ON ARTIFICIAL INTELLIGENCE IN INDONESIA’S PUBLIC SECTOR: REIMAGINING DIGITAL GOVERNMENT Pratiwi, Aprilia; Rahmawyanet, Mahsa Elvina; Wibowo Putra, Prasetyo Adi; Sensuse, Dana Indra
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6842

Abstract

This study conducts a Systematic Literature Review (SLR) to critically examine the application of Artificial Intelligence (AI) in e-government within the Indonesian public sector. Addressing the limited empirical research and fragmented understanding of AI adoption in Indonesia’s digital governance landscape, this review analyzes 22 peer reviewed articles published between 2021 and 2025 from reputable databases including Scopus, IEEE, ACM Digital Library, SpringerLink, and Emerald Insight. The review identifies adaptability and innovation, ethical consideration, collaboration and partnership as the most frequently cited critical success factors. Meanwhile, the top three recurring challenges are lack of awareness, skill & expertise, policy or legal uncertainty, resistance to change. To address these challenges, the study proposes a multi dimensional AI implementation strategy focusing on strengthening digital infrastructure, developing human capital through sustained capacity building, formulating clear and accountable AI governance policies, and fostering inclusive, cross sectoral stakeholder engagement. This study offers novel insights by mapping AI related factors into the Technology,Organization, Environment (TOE) framework and synthesizing practical, context-specific recommendations for Indonesian policymakers seeking to build an adaptive, inclusive, and sustainable AI based e-government ecosystem
OPTIMIZATION OF SVM ALGORITHM FOR OBESITY CLASSIFICATION WITH SMOTE TECHNIQUE AND HYPERPARAMETER TUNING Nur, Khairun Nisa Arifin; Wanto, Anjar; Poningsih, Poningsih
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6878

Abstract

Excessive fat accumulation that impairs personal health and raises the risk of chronic diseases is the hallmark of obesity, a global health issue. Decision Tree (DT) has been widely used for obesity classification, but it tends to suffer from overfitting and poor performance on imbalanced datasets. To overcome these limitations, this study proposes an optimization of the Support Vector Machine (SVM) algorithm using Synthetic Minority Over-sampling Technique (SMOTE) and Hyperparameter Tuning. SMOTE was applied to balance the class distribution, whereas Grid Search was utilized to determine the optimal combination of hyperparameters (C, gamma, and kernel). The dataset employed in this research comprises multiple features related to individual health and lifestyle, with obesity level as the target class. The experimental results demonstrate that the optimized SVM model demonstrated strong classification performance, attaining 97% in accuracy, precision, recall, and F1-score. This high performance is significant because it enables more accurate early detection of obesity risk, which can support timely medical intervention and personalized treatment planning, ultimately contributing to better public health outcomesThese findings suggest that incorporating SMOTE and Hyperparameter Tuning substantially improves SVM performance, establishing it as a robust approach for obesity classification and early risk detection.
COMPARATIVE ANALYSIS OF CLASSIFICATION ALGORITHMS IN HANDLING IMBALANCED DATA WITH SMOTE OVERSAMPLING APPROACH Nugroho, Agung; Wiyanto; Maulana, Donny
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6956

Abstract

Most machine learning algorithms tend to yield optimal results when trained on datasets with balanced class proportions. However, their performance usually declines when applied to data with significant class imbalance. To address this issue, this study utilizes the Synthetic Minority Oversampling Technique (SMOTE) to improve class distribution before model training. Several classification algorithms were employed, including Decision Tree, K-Nearest Neighbors, Logistic Regression, Support Vector Machine, and Random Forest. Experimental results reveal that the Random Forest model produced the highest accuracy (95.70%) and the best F1-score, demonstrating a well-balanced trade-off between precision and recall. In contrast, the Logistic Regression algorithm achieved the highest recall (74.20%), indicating better sensitivity in identifying positive instances despite a lower F1-score. These outcomes highlight the importance of choosing appropriate classification methods based on the specific evaluation goals whether prioritizing accuracy, recall, or overall model balance.
DEVELOPMENT OF A SMART IOT-BASED MONITORING SYSTEM FOR FERTIGATION AND SEED WEIGHT DETECTION IN SACHA INCHI Prasetyo, Tri Ferga; Purwanto, Muhamad Dendi; Sujadi, Harun; Andayani, Sri
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6968

Abstract

This research focuses on designing a fertilization monitoring system based on the Internet of Things (IoT) and detecting the weight of Sacha Inchi plant seeds. The two tools are integrated with IoT platforms, enabling remote monitoring and control via the Simosachi app. Test results indicate that the system provides accurate data on soil and plant conditions, allowing farmers to make informed decisions on fertilization and irrigation. The seed weight detection tool also functions well, with a minor error margin still within acceptable limits. With improved monitoring and control of the fertilization process, as well as accurate monitoring of crop yields, the system is expected to help farmers achieve more optimal harvests. The seed weight detection tool achieved an accuracy of 97.94%, surpassing similar prior systems in terms of real-time data integration and multi-parameter monitoring. Future research may focus on enhancing the accuracy of the seed weight detection tool and developing advanced fertigation control algorithms
MONITORING ELDERLY HEART RATE BASED ON OXIMETER SENSORS Retnoningsih, Endang; Rofiah, Syahbaniar; Arofah, Sendi Rifa
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6970

Abstract

Heart rate check is an important step in preventing heart attacks that is often not realized by the elderly. However, independent heart rate checks by the elderly have not utilized technology, especially Android. This study design a heart rate detector using the Max30102 Oximeter Sensor integrated with Android device from the elderly aged 60 to 75 years and displays the results of the heart rate per minute (BPM) along with normal or abnormal status on the Android application. The prototype method involves the stages of development, testing, and evaluation of the tool. The results of the study showed that this heart rate detector was able to provide data on heart rate per minute (BPM) that was accurate and easily accessible to the elderly, so that the elderly could check their health independently. The test results indicate a detection accuracy of 97% with a standard deviation of 1.19 BPM, which is higher compared to studies using the Max30100. Thus, this tool is expected to help increase the independence of the elderly in monitoring heart health and reduce the risk of heart attack through routine monitoring
IMPROVING HANDWRITTEN DIGIT RECOGNITION USING CYCLEGAN-AUGMENTED DATA WITH CNN–BILSTM HYBRID MODEL Utomo, Fandy Setyo; Barkah, Azhari Shouni; Muhtyas Yugi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6982

Abstract

Handwritten digit recognition presents persistent challenges in computer vision due to the high variability in human handwriting styles, which necessitates robust generalization in classification models. This study proposes an advanced data augmentation strategy using Cycle-Consistent Generative Adversarial Networks (CycleGAN) to improve recognition accuracy on the MNIST dataset. Two architectures are evaluated: a standard Convolutional Neural Network (CNN) and a hybrid model combining CNN for spatial feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) for sequential pattern modeling. The CycleGAN-based augmentation generates realistic synthetic images that enrich the training data distribution. Experimental results demonstrate that both models benefit from the augmentation, with the CNN-BiLSTM model achieving the highest accuracy of 99.22%, outperforming the CNN model’s 99.01%. The study’s novelty lies in the integration of CycleGAN-generated data with a CNN–BiLSTM architecture, which has been rarely explored in previous works. These findings contribute to the development of more generalized and accurate deep learning models for handwritten digit classification and similar pattern recognition tasks.
YOLO MODEL DETECTION OF STUDENT NEATNESS BASED ON DEEP LEARNING: A SYSTEMTIC LITERATURE REVIEW Saryoko, Andi; Aziz, Faruq
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6986

Abstract

Maintaining proper student neatness (uniform compliance, grooming standards, and posture) is essential for fostering disciplined learning environments. While traditional monitoring methods are labor-intensive and subjective, computer vision-based solutions leveraging You Only Look Once (YOLO) architectures offer promising alternatives. The objective of this study is to evaluate YOLO optimization techniques for student neatness detection, identify key challenges, and propose relevant future research directions. This systematic review evaluates 28 recent studies (2021-2024) to analyze optimization techniques for YOLO models in student neatness detection applications. Key findings demonstrate that attention-enhanced variants (e.g., YOLOv10-MSAM) achieve 87.0% mAP@0.5, while pruning and quantization methods enable real-time processing (50-130 FPS) on edge devices like Jetson Orin. The analysis reveals three critical challenges: (1) occlusion handling in crowded classrooms (10-15% false negatives), (2) lighting/background variability, and (3) ethical concerns regarding facial recognition. Emerging solutions include hybrid vision-language models for explainable detection and federated learning for privacy preservation. The review proposes a taxonomy of optimization approaches categorizing architectural modifications (attention mechanisms, lightweight backbones), data augmentation strategies (GAN-based synthesis), and deployment techniques (TensorRT acceleration). Future research directions emphasize multi-modal sensor fusion and domain adaptation for cross-institutional generalization. This work provides educators and AI developers with evidence-based guidelines for implementing automated neatness monitoring systems while addressing practical constraints in educational settings.
SCRUM AND ITIL-BASED SUPPORT SYSTEM DESIGN AND IMPLEMENTATION AT RAPHA THERESIA HOSPITAL Kasrizal; Sharipuddin; Devitra, Joni; Gunardi
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.v11i3.7004

Abstract

This research addresses the challenges of manual IT service management at Rapha Theresia Hospital, where existing processes lacked systematic tracking and reporting, leading to operational inefficiencies. The purpose was to design and implement a web-based IT support system for systematic documentation of IT requests and repairs, integrating the Scrum agile development methodology with the ITIL framework, and enabling comprehensive IT performance reporting for management evaluation. The study employed a hybrid methodological approach, combining Scrum for iterative development and ITIL for robust service delivery. Research methods included problem identification, and iterative implementation across four sprints with defined Service Level Agreements (SLAs). Rigorous User Acceptance Testing (UAT) validated the system's functionality. Results show successful implementation of a centralized system managing IT requests, assets, and reports, significantly improving operational efficiency, service reliability, and fostering data-driven decision-making. The system enhanced coordination, transparency, and accelerated service resolution within the IT team.

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