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TIN: TERAPAN INFORMATIKA NUSANTARA
Published by Forum Kerjasama Pendidikan TInggi
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Jurnal TIN: TERAPAN INFORMATIKA NUSANTARA memuat tentang Kajian Bunga Rampai dari berbagai ide dan hasil penelitian para peneliti, mahasiswa, dan dosen yang berkompeten di bidangnya dari berbagai disiplin ilmu seperti: Komputer, Informatika, Industri, Elektro, Telekomunikasi, Kesehatan, Agama, Pertanian, Pembelajaran, Pendidikan, Teknologi Pendidikan, Ekonomi dan Bisnis, Manajemen, Akuntansi, dan Hukum
Arjuna Subject : Umum - Umum
Articles 785 Documents
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Implementasi Hybrid Feature Fusion menggunakan ResNet50 dan RGB-HSV untuk Klasifikasi Indikasi Tingkat Kepedasan Cabai Berdasarkan Karakteristik Visual Febriyan Biopsa Minanda
TIN: Terapan Informatika Nusantara Vol 7 No 3 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i3.10695

Abstract

Chili is a horticultural commodity with diverse visual characteristics and different levels of pungency, requiring an objective and consistent classification method. A challenge in chili classification is the similarity of visual characteristics across categories and the separate use of color features or deep learning features in previous studies, resulting in less comprehensive information representation. This study aims to implement hybrid feature fusion using ResNet50 and RGB-HSV color features to classify indications of chili pungency into Hot, Medium, and Mild categories based on visual characteristics. The novelty of this study lies in integrating deep features from ResNet50 with RGB-HSV features into a single feature representation, as ResNet50 can capture complex visual patterns, while RGB-HSV explicitly provides color information. This study employed a quantitative approach with an experimental methodology based on image processing and deep learning. The dataset consisted of 1,592 chili images, including 416 Hot, 514 Medium, and 662 Mild images. The data were divided at a 70:15:15 ratio into 1,114 training, 239 validation, and 239 testing images. Preprocessing included background removal using rembg, resizing images to 224 × 224 pixels, and extracting mean and standard deviation features from RGB and HSV channels. The model achieved 96.23% accuracy, 96.10% precision, 96.08% recall, and a 96.08% F1-score, outperforming the original image model. These findings indicate that integrating deep and color features improves chili classification performance.
Rekonstruksi Aktivitas Pengguna Situs Judi Online pada Smartphone Android melalui Analisis Digital Forensik Aji Pangestu; Teri Mengkasrinal; Balthasar Sebastian Lumbantobing
TIN: Terapan Informatika Nusantara Vol 7 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i2.10709

Abstract

Android smartphones retain digital artifacts that can help explain user activity, but modern Android security mechanisms restrict access to internal application data. This study identifies artifacts obtainable from a non-rooted Android 11 smartphone and evaluates their ability to support the reconstruction of online gambling website access. A descriptive qualitative method with a simulation-based digital forensic case study was conducted in Setiadarma, Tambun Selatan, Bekasi, from May to July 2026. The test device was a non-rooted Redmi 10A running Android 11. Acquisition was performed using MOBILedit Forensic, the extracted data were preserved with FTK Imager and examined with Autopsy, while Android Debug Bridge was used to validate connectivity and collect netstats and batterystats. Google Activity documentation displayed a visit entry to the simulated site on the Redmi 10A, while netstats and batterystats supported the use of Google Chrome. However, browsing history, cookies, cache, browser databases, downloaded files, screenshots, and related metadata were not found in the acquisition output. Google Activity was treated as account-level supporting evidence rather than an internal Chrome artifact recovered through non-root acquisition. Consequently, the reconstruction remained partial: Chrome use and an indication of the site visit could be supported, but a complete browser timeline could not be recovered.
Komparasi Efektivitas PCC dan ECMP pada Arsitektur Multi-ISP Heterogen Ditinjau dari Parameter Quality of Services (QoS) Reza Dwi Cahya Kurniawan; Riska Wibowo
TIN: Terapan Informatika Nusantara Vol 7 No 3 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i3.10782

Abstract

Fast and stable internet access is essential to support academic and administrative activities at Universitas Muhammadiyah Tegal. To overcome bandwidth fluctuations and ensure optimal connectivity, implementing a multi-ISP load balancing system is required. This study aims to compare the performance of Per Connection Classifier (PCC) and Equal-Cost Multi-Path (ECMP) load balancing algorithms based on TIPHON Quality of Service (QoS) standards at Universitas Muhammadiyah Tegal. The methodology utilizes a quantitative experimental approach on a MikroTik router with a dual-WAN configuration (2 ISPs × 100 Mbps). Testing was conducted over 7 days during peak hours (08:00–16:00 WIB) using iPerf3 for traffic injection and Wireshark for packet analysis. Quantitative results reveal that the PCC algorithm achieves superior performance across all QoS parameters, recording a throughput of 194.8 Mbps (97.4% efficiency), a delay of 28.5 ms, a jitter of 3.4 ms, and a packet loss of 0.3%. In contrast, ECMP produced a jitter of 11.6 ms and a packet loss of 2.8%, triggering session drops on critical services. PCC’s advantage is driven by its session locking mechanism via precise mangle rules, effectively mitigating bufferbloat and hash collisions. Based on a comprehensive evaluation of throughput, delay, jitter, and packet loss, the PCC algorithm is objectively superior and strongly recommended for implementation at Universitas Muhammadiyah Tegal.
Analisis Data Transaksi Konsumen Menggunakan Algoritma Apriori untuk Strategi Promosi pada Grosir Sembako Vera Oktaviani; Joko Priambodo
TIN: Terapan Informatika Nusantara Vol 7 No 3 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i3.10787

Abstract

Wholesale grocery businesses generate an increasing amount of consumer transaction data, but such data has not been optimally utilized to identify purchasing patterns and relationships among products as a basis for developing promotional strategies. This condition means that promotional decisions may still be made based on general observations of frequently sold products, making it difficult to systematically identify opportunities from consumer purchasing patterns. This study aims to analyze the relationships among products in consumer transaction data using the Apriori algorithm and to generate promotional strategy recommendations in the form of bundling and cross-selling. The data consist of 350 consumer transactions involving nine product categories. The 350 transactions were used because they represent the entire transaction data available during the research observation period, thereby reflecting the transaction conditions of the research object during that period. Data processing was conducted using the Knowledge Discovery in Databases (KDD) stages, namely selection, preprocessing, transformation, data mining, and evaluation. The Apriori algorithm was applied with a minimum support of 30% and a minimum confidence of 70%. The analysis produced 9 frequent 1-itemsets, 21 frequent 2-itemsets, 3 frequent 3-itemsets, and 22 association rules that met the minimum thresholds. The rule with the highest confidence was Oil and Flour → Eggs, with a support of 48.57% and a confidence of 92.39%. The validation results showed that the manual calculations and system results produced consistent values. Based on the obtained association rules, promotional strategy recommendations can be directed toward bundling and cross-selling according to the confidence level of each rule. This study demonstrates that the Apriori algorithm can be used to identify purchasing patterns from wholesale transaction data and generate information that can support the development of data-driven promotional strategies. However, the results are limited to 350 transactions and do not directly measure the impact of implementing the promotional strategies on sales.
Perancangan Rekam Medik Elektronik Pasien Jiwa dan Evaluasi Usability dengan System Usability Scale (SUS) Galank Rasta Fariant; Wahyu Teja Kusuma; M. Syauqi Haris
TIN: Terapan Informatika Nusantara Vol 7 No 3 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i3.10792

Abstract

The development of information technology drives the transformation of medical record management into Electronic Medical Records (EMR), including in the rehabilitation services for patients with mental disorders, which require continuous recording of psychological conditions, therapy progress, and service history. At the Yayasan Ketenangan Jiwa dan Hati (YKJH), the recording process is still done manually, which has the potential to cause recording errors, delays in information access, and difficulties in managing patient data. This research aims to design an RME interface that meets the needs of users in mental health patient rehabilitation services and to evaluate the usability level of the prototype using the System Usability Scale (SUS). The research applies the Waterfall method, which includes requirements analysis, system design, prototype implementation, and usability evaluation. Data were collected thru observation, interviews, and literature review. The resulting prototype provides features such as login, dashboard, patient registration, patient data management, diagnosis, SOAP notes, prescriptions, medical history, reports, and user management. Usability evaluation was conducted on five respondents consisting of two administrative staff, two medical personnel, and one foundation manager thru scenarios using the main features of the system, followed by filling out the SUS questionnaire. The evaluation results obtained an average score of 84.0, which falls into the Excellent and Acceptable categories. The contribution of this research is to produce an RME design tailored to the needs of administration and rehabilitation services for patients with mental disorders, as well as to provide a usability evaluation as a basis for further system development. The research results indicate that the prototype has a good level of usability in the initial evaluation stage, although further testing with a larger number of users and in a direct operational environment is still needed.
Integrasi Fuzzy Mamdani dan Certainty Factor pada Sistem Pakar Prediksi Penyakit Jantung Naza Riski Romah Doni; Hadi Zakaria
TIN: Terapan Informatika Nusantara Vol 7 No 3 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i3.10807

Abstract

Heart disease is a leading cause of death globally; therefore, a rapid and structured initial screening process is essential to minimize the risk of delayed treatment. The PT. XYZ clinic faces challenges in conducting initial heart disease screenings due to limited diagnostic equipment and an identification process that relies heavily on the subjective assessment of medical personnel. This study aims to implement a web-based expert system that integrates the Fuzzy Mamdani method and the Certainty Factor method to assist in the initial screening of heart disease. The Fuzzy Mamdani method processes symptom values ​​into membership degrees and generates alpha-predicate values ​​through fuzzification and inference processes, while the Certainty Factor method calculates the confidence level based on alpha-predicate values ​​and expert-assigned confidence weights for each rule. The system's knowledge base comprises 14 symptoms, three types of heart disease, and 190 rules derived through a knowledge acquisition process with cardiologists. The contribution of this study lies in the implementation of Fuzzy Mamdani and Certainty Factor integration, in which the alpha-predicate value resulting from fuzzy inference is used as the rule activation level and combined with the expert CF to determine the system confidence level for initial heart disease screening. Testing was conducted using 30 case scenarios and evaluated via a confusion matrix. The results demonstrate that all scenarios were successfully classified according to the reference labels, achieving a 100% match rate with the established knowledge base. These findings indicate that the system consistently executes inference and confidence level calculations as a tool for initial heart disease screening, although it is not intended to replace a physician's diagnosis or clinical decision-making.
Model Konseptual Penguatan Instrumen Penilaian Kematangan Keamanan Siber (IKAS) menggunakan Pendekatan Sosioteknis melalui Pemetaan Keselarasan terhadap NIST CSF 2.0 dan ISO/IEC 27001:2022 Muhammad Arif Ali Wasi; Agung Budi Susanto; Winarni Winarni
TIN: Terapan Informatika Nusantara Vol 7 No 3 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i3.10813

Abstract

The Cybersecurity Maturity Assessment Instrument (IKAS) underpins the protection roadmap of Vital Information Infrastructure in Indonesia, so any weakness in its design propagates directly into national policy. Its alignment with international frameworks and the balance of its sociotechnical composition, however, have never been examined academically. This study maps the alignment of IKAS version 1.2.1 against NIST CSF 2.0 and ISO/IEC 27001:2022 bidirectionally, diagnoses the gaps through Leavitt’s Diamond, and designs a conceptual strengthening model named IKAS-ST. A descriptive qualitative content analysis was applied to 181 IKAS items, 106 NIST CSF 2.0 subcategories, and 93 ISO/IEC 27001:2022 Annex A controls. The instrument proves substantially aligned, with an average forward coverage of 81.2% (Largely Achieved), yet 12 white spots remain, concentrated in the governance and recovery functions. The decisive finding is that Task is the only under-represented scope, with an extreme deficit in the Detection domain where items measure the ownership of detection technology rather than the procedures that operate it. The merit of IKAS-ST lies not in adding items but in three testable properties: every added item is traceable to an identified white spot, so no addition is speculative; the restructuring addresses the root cause, namely the absence of an explicit procedural scope; and all 181 original items are retained, preserving the comparability of historical assessment results. The model is conceptual and awaits expert validation and field testing.
Perancangan UI/UX Aplikasi Pendaftaran Siswa RA Berbasis Web Menggunakan Design Thinking dan Evaluasi SUS Amelia Khairani; Fince Tinus Waruwu; Melda Panjaitan
TIN: Terapan Informatika Nusantara Vol 7 No 3 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i3.10826

Abstract

The student admission process at RA Tahfiz Qur'an Darussalam is still conducted manually, resulting in inefficient information dissemination, form completion, document submission, and data management, while also increasing the risk of recording errors. This study aims to design a User Interface (UI) and User Experience (UX) for a web-based student admission application that meets user needs. The research employed the Design Thinking method, consisting of the Empathize, Define, Ideate, Prototype, and Test stages. The Empathize stage was conducted through observations and interviews to identify user needs, followed by the Define stage to formulate design solutions. The proposed design was then visualized into wireframes and high-fidelity prototypes using Figma during the Prototype stage. The usability evaluation was carried out using the System Usability Scale (SUS) involving 30 respondents, consisting of parents, guardians of prospective students, and a school administrator. The evaluation results produced an average SUS score of 88.92, which falls into the Acceptable category, achieves Grade Scale B, and is classified as Excellent on the Adjective Rating scale. These findings indicate that the proposed UI/UX design has a high level of usability, is easy to understand, and effectively supports users in completing the web-based student admission process.
DenseNet121 sebagai Feature Extractor pada Denoising Autoencoder untuk Deteksi Anomali Unsupervised Citra X-Ray Dada Raihan Muhammar Zikra; Febi Yanto; Benny Sukma Negara; Surya Agustian; Reski Mai Candra
TIN: Terapan Informatika Nusantara Vol 7 No 3 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i3.10860

Abstract

Anomaly detection in chest X-ray images remains a challenge in medical imaging, as supervised learning approaches require large amounts of labeled data that are difficult and costly to annotate. This study proposes an unsupervised learning anomaly detection system that integrates a pretrained DenseNet121 as a feature extractor with a Denoising Autoencoder (DAE), so that training requires only normal images without anomaly annotations. The model was trained on normal images and tested on COVID-19 and pneumonia images to evaluate its anomaly detection capability based on reconstruction error relative to an optimal threshold. Evaluation was conducted on the Covid19-Pneumonia-Normal Chest X-Ray Images dataset comprising 5,228 images, comparing the performance of DenseNet121 and ResNet50 as feature extractors across three latent dimension configurations. The DenseNet121 configuration with a latent dimension of 128 achieved the highest overall performance on most metrics, namely 91% accuracy, 90.75% sensitivity, 72% Macro F1-Score, and a validation loss (MSE) of 0.0952 on 3,608 test images, although its AUC (0.9526) and specificity (87.36%) were not consistently the highest among all tested configurations. These results demonstrate that using DenseNet121 as a feature extractor improves the DAE's ability to distinguish normal from anomalous lung images, suggesting its potential as an efficient preliminary screening approach under conditions of limited labeled data.
Deteksi Anomali pada Citra X-ray Dada Menggunakan Variational Autoencoder dengan Skema Sequential Hyperparameter Optimization Diki Aulio Fransisko; Febi Yanto; Benny Sukma Negara; Siti Ramadhani; Reski Mai Candra
TIN: Terapan Informatika Nusantara Vol 7 No 3 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i3.10864

Abstract

The limited availability of labeled medical images remains a major challenge in developing reliable deep learning-based disease detection systems. Conventional classification approaches generally require a large amount of abnormal data, whereas medical image annotation is time-consuming, costly, and highly dependent on radiological expertise. This study proposes an unsupervised anomaly detection model for chest X-ray images using a Variational Autoencoder (VAE), in which only normal images are utilized during the training process. The experiments were conducted on the COVID-19-Pneumonia-Normal Chest X-ray Images dataset, consisting of 5,228 images categorized into normal, pneumonia, and COVID-19 classes. The proposed framework includes image preprocessing, baseline VAE construction, sequential hyperparameter optimization, Beta-VAE implementation, and model evaluation using Accuracy, Precision, Recall, F1-Score, and the Area Under the Receiver Operating Characteristic Curve (AUROC). Experimental results demonstrate that the optimized model outperformed the baseline model, achieving an Accuracy of 97.50%, Precision of 96.32%, Recall of 100%, F1-Score of 98.12%, and an AUROC of 0.9999. These findings indicate that hyperparameter optimization and appropriate β coefficient selection improve latent representation learning, leading to more effective discrimination between normal and abnormal chest X-ray images. Therefore, the proposed approach has the potential to serve as an artificial intelligence-based early screening tool for chest radiograph analysis, particularly in scenarios where labeled medical data are limited.

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