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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 756 Documents
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Penerapan Semi-Supervised Deep Learning dengan Remixmatch untuk Klasifikasi Penyakit Paru-Paru Menggunakan Citra Chest X-Ray Fajri Fajri; Benny Sukma Negara; Muhammad Irsyad; Febi Yanto; Iis Afrianty
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.v7i2.10382

Abstract

Lung diseases such as pneumonia and COVID-19 viral infection remain significant health problems that require fast and accurate diagnostic processes. The utilization of deep learning-based Computer-Aided Diagnosis (CAD) on Chest X-Ray (CXR) images has demonstrated promising capabilities in assisting disease classification. However, the implementation of deep learning models in the medical field still faces a major challenge, namely the limited availability of labeled data due to the time-consuming annotation process and the involvement of medical experts. This study applies a semi-supervised learning approach using the ReMixMatch algorithm with DenseNet169 architecture as a feature extraction backbone to reduce the dependency on large amounts of labeled data. Experiments were conducted using the public dataset Covid19-Pneumonia-Normal Chest X-Ray Images available on Mendeley Data. The ReMixMatch method utilizes both labeled and unlabeled data through pseudo-labeling, distribution alignment, MixUp augmentation, and consistency regularization mechanisms during the model training process. The evaluation was performed using several labeled data scenarios, namely 10, 20, 30, and 40 labels per class. The experimental results show that the combination of ReMixMatch and DenseNet169 achieved high classification performance with an accuracy of 96.43% on the validation data. The model evaluation obtained a precision value of 96.47%, a recall value of 96.43%, and an F1-score of 96.42%. These results indicate that the semi-supervised learning approach is able to effectively utilize information from unlabeled data, thereby maintaining high Chest X-Ray image classification performance under limited annotation conditions. This study offers an alternative approach to developing a chest X-ray image classification system through the application of the ReMixMatch algorithm combined with the DenseNet169 architecture, enabling the model to achieve good classification performance even with a limited amount of labeled data.
Perbandingan Algoritma K-Means dan Fuzzy C-Means pada Segmentasi Citra Biji Jengkol Deteksi Kematangan Entin Monika; Harry Witriyono
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.v7i2.10389

Abstract

This study aims to compare the performance of K-Means and Fuzzy C-Means (FCM) algorithms in image segmentation of jengkol seeds (Archidendron pauciflorum) for automatic ripeness detection. The dataset comprises 300 images categorized into three ripeness classes: ripe (100 images), half-ripe (100 images), and unripe (100 images). Images were acquired using a 12 MP smartphone camera at a standardized resolution of 640×480 pixels under controlled lighting at a distance of 20 cm from the object. The research pipeline includes image preprocessing (RGB-to-HSV and LAB/CIELAB color space conversion, median filter noise reduction, and contrast enhancement), K-Means and FCM segmentation, color and texture feature extraction using the Gray Level Co-occurrence Matrix (GLCM), and performance evaluation based on accuracy, Peak Signal-to-Noise Ratio (PSNR), and computational time. Results indicate that FCM achieves 90–93% accuracy and 30–32 dB PSNR, outperforming K-Means (85–88% accuracy, 27–29 dB PSNR). Nevertheless, K-Means excels in computational efficiency (0.45 s vs. 1.20 s). FCM is recommended for high-accuracy applications, whereas K-Means is preferred when computational efficiency is prioritized.
Pengembangan Sistem Informasi Manajemen Data Gapoktan Menggunakan Metode Fountain Eva Argarini Pratama; Ahmad Nouvel; Sutrisno Sutrisno
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.v7i2.10406

Abstract

Data management in Farmers Group Associations (Gapoktan) is still largely done manually, causing various problems, such as data duplication, difficulty in finding information, delays in preparing reports, and a high risk of document loss. This condition also exists in Gapoktan in the Sokaraja area, Banyumas Regency, which requires an integrated system to support more effective and efficient organizational data management. This study aims to develop a web-based Gapoktan Data Management Information System using the Fountain method as a software development model. The study used a Research and Development (R&D) approach with the research location at Gapoktan in the Sokaraja area, Banyumas. Data collection was carried out through observation, interviews, and documentation studies. The research respondents numbered 18 people selected using a purposive sampling technique, consisting of the chairperson, secretary, and treasurer of Gapoktan. System testing was conducted using Black Box Testing to test system functionality, while user evaluation was conducted through questionnaires to measure ease of use, system effectiveness, work efficiency, and user satisfaction. The results showed that all system functions ran as needed with a test success rate of 100%. The user evaluation results obtained an average score of 90.00%, categorized as very good, consisting of aspects of ease of use of 91.11%, system effectiveness of 88.89%, work efficiency of 86.67%, and user satisfaction of 93.33%. These results indicate that the developed system is able to improve the quality of data management, accelerate the administrative process, and support more effective report preparation within the Gapoktan environment.
Pengembangan Gim Edukasi Pengenalan Kosakata Multibahasa Berbasis Web untuk Anak Usia Dini Menggunakan Metode Multimedia Development Life Cycle (MDLC) M. Fachri Aji Bintang Prasetyo; Kurniawan Dwi Irianto
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.v7i2.10415

Abstract

Language development is a fundamental aspect of early childhood growth, particularly for children aged 4–6 years, a period widely recognized as the golden age. However, multilingual vocabulary learning in Early Childhood Education (ECE) institutions still faces several challenges, including the predominance of conventional learning methods, limited interactivity, and insufficient ability to maintain children's motivation and engagement throughout the learning process. As a result, vocabulary acquisition across multiple languages has not been optimally achieved. This study aims to develop a web-based multilingual vocabulary learning game for early childhood using the Multimedia Development Life Cycle (MDLC) method, which consists of six stages: Concept, Design, Material Collecting, Assembly, Testing, and Distribution. The game was developed using the Next.js framework with TypeScript and features five game modes: Matching Pairs, Treasure Box, Quiz Time, Word Builder, and Audio Match, supporting three languages: Indonesian, English, and Arabic. The study was conducted at PG-TK Islam Al-Azhar Cairo Yogyakarta, involving 14 six-year-old children as observation participants and two teachers as respondents for the System Usability Scale (SUS) evaluation. Functional testing using Black Box Testing across 21 test scenarios showed that all system functions operated successfully. The usability evaluation resulted in an average SUS score of 81.25, which falls into the Good and Acceptable categories. Furthermore, observations indicated an average game usage achievement of 93.57% and an average vocabulary recognition achievement of 92.86%, both classified as Very Good. These findings demonstrate that the developed game is feasible as a multilingual vocabulary learning medium for early childhood education in terms of functionality, usability, children's engagement, and effectiveness in supporting vocabulary acquisition.
Sistem Monitoring Energi Genset pada Stasiun Penyiaran TVRI Berbasis IoT Suci Ramadhani; Deitje Sofie Pongoh; Arnold Robert Rondonuwu
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.v7i2.10440

Abstract

The availability of electrical energy is a crucial factor in maintaining the continuity of broadcasting station operations. Public Broadcasting Institution (LPP) TVRI North Sulawesi, where a generator set (genset) is used as a backup power source during interruptions to the main electrical supply. The primary challenge is the limited capability of conventional monitoring systems to observe electrical parameters, including current, power, energy consumption, and power source status, in real time. This study aims to develop an Internet of Things (IoT)-based generator energy monitoring system using an ESP32 microcontroller, SCT-013 current sensor, Liquid Crystal Display (LCD), and cloud-based monitoring platforms. The proposed system measures electrical parameters in real time and transmits the data via a Wi-Fi network to the Blynk application and Google Spreadsheet, enabling remote monitoring by operators. Experimental results show that the proposed system measures electrical current with a measurement error ranging from 1% to 8%, yielding an average error of 4.17% and an average measurement accuracy of 95.83% compared with a reference clamp meter. The system also successfully calculates electrical power up to 2,640 W at a current of 15 A, automatically records monitoring data in Google Spreadsheet, and displays real-time information through the Blynk application when a Wi-Fi connection is available. Furthermore, the system accurately detects PLN ON, Generator ON, and both sources OFF conditions based on current variations in the Automatic Transfer Switch (ATS). The main contribution of this study is the development of an integrated IoT-based generator energy monitoring system capable of measuring electrical current, calculating power and energy consumption, detecting PLN–generator switching status through the ATS, storing historical data in Google Spreadsheet, and providing real-time monitoring and notifications via the Blynk platform to support reliable energy management in broadcasting stations. These results demonstrate that the proposed system provides reliable real-time generator monitoring and improves the effectiveness of monitoring, data logging, and maintenance activities, thereby enhancing the reliability of the electrical system at TVRI North Sulawesi Broadcasting Station.
Implementasi Tata Kelola Teknologi Informasi dalam Transformasi Digital Manajemen Sumber Daya Manusia: Tinjauan Literatur Marat Martypasha; Satria Justicio; Muhammad Hildan Afri Zakaria; Birra Sodaq Muttaqi Devari; Rifat Fachrial Farhan; Fikri Rizany Sahdana Putra
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.v7i2.10454

Abstract

In the era of rapidly accelerating digital transformation, organizational success in adopting technology for human resource management (HRM) depends not only on technological infrastructure but also on effective IT governance that bridges strategy and implementation. This study addresses the persistent execution gap between strategic HRIS/MIS/ICT adoption policies and their implementation by HR staff, caused by limited digital competencies, weak operational guidelines, and inadequate adaptive governance. It aims to identify factors influencing IT implementation effectiveness in HRM, digital competency development strategies integrated with IT governance, and governance frameworks supporting digital HRM transformation. A Systematic Literature Review (SLR) following the PRISMA 2020 protocol was conducted. From 865 articles retrieved from the ScienceDirect database, 41 studies met the eligibility criteria and were analyzed. The findings indicate that IT implementation effectiveness is shaped by four key factors: digital transformational leadership, infrastructure and data governance readiness, adaptive organizational culture, and technical-ethical risk management. Effective digital competency development combines structured reskilling/upskilling programs, AI-enabled adaptive learning, and the enhancement of AI literacy alongside uniquely human skills. Furthermore, successful digital HRM transformation requires integrating ambidextrous governance, ethical data governance, and maturity model-based roadmaps. This study proposes the IT Governance–HRM Digital Transformation Integration Framework (IGHDTIF) as a holistic conceptual model integrating these dimensions and addressing a gap identified in the reviewed literature.
Analisis Performa dan Efisiensi VGG16, ResNet50, dan MobileNetV2 pada Klasifikasi Citra Jajanan Tradisional Ni Luh Widi Rahayu; Ni Kadek Bumi Krismentari; I Kayan Herdiana
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.v7i2.10491

Abstract

Previous research on traditional food image classification demonstrated that the VGG16 architecture could achieve good classification performance. However, the study focused on a single architecture and did not provide a comprehensive comparison between predictive performance and computational efficiency across multiple models. This limitation highlights the need to evaluate other architectures in order to identify a model that is not only accurate but also suitable for different deployment environments. This study aims to compare the performance and efficiency of three transfer learning architectures, namely VGG16, ResNet50, and MobileNetV2, for traditional food image classification. The dataset consisted of 2,445 images grouped into ten classes: batun bedil, bubur injin, jaje lukis, jaje piling, jaje wajik, kaliadrem, klepon, laklak, ongol-ongol, and pisang rai. The dataset was divided using a 70:15:15 ratio into 1,706 training images, 363 validation images, and 376 testing images. All models were trained under the same configuration, using an input size of 224 × 224 pixels, batch size of 64, Adam optimizer, learning rate of 0.0001, categorical crossentropy loss, data augmentation, and 10 epochs. Model evaluation was conducted using accuracy, precision, recall, F1-score, training time, model size, and inference time per image. The results showed that ResNet50 achieved the best classification performance, with an accuracy of 84.04%, precision of 85.15%, recall of 85.16%, and F1-score of 84.89%. MobileNetV2 achieved an accuracy of 82.45% with the smallest model size of 12.98 MB, while VGG16 obtained an accuracy of 73.67%. These findings indicate that ResNet50 is more suitable for systems that prioritize classification performance, whereas MobileNetV2 is more appropriate for applications requiring a lightweight model. The contribution of this study lies in the comparative evaluation of performance and efficiency across three architectures under the same experimental setting, resulting in practical model recommendations based on deployment requirements.
Pengembangan FMEWS Berbasis IoT dan MQTT untuk Peringatan Banjir I Komang Darma Wiguna; Ketut Agus Seputra; Kadek Yota Ernanda Aryanto
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.v7i2.10499

Abstract

Floods are disasters that frequently cause physical, social, and economic losses, including in Buleleng Regency, Bali. The main problem in flood mitigation lies in delayed early warning delivery and the absence of a centralized platform that integrates field monitoring data with operational disaster management data. This study aims to design and develop the Flood Monitoring and Early Warning System (FMEWS) based on the Internet of Things (IoT) and the Message Queuing Telemetry Transport (MQTT) protocol to support real-time flood monitoring and early warning. The system was developed using the Waterfall model of the System Development Life Cycle (SDLC) and consists of IoT devices, an IoT Gateway, and a web-based information system integrating water level monitoring, early warning notifications, shelter management, public facility management, community reports, and spatial visualization. The novelty of this study lies in integrating real-time IoT-based flood monitoring with operational disaster management data into a single centralized platform. The results show that a transmission rate of 2–5 Hz provides the best balance between latency and communication reliability, with a Packet Delivery Ratio (PDR) of at least 96.67%. Usability testing involving 22 respondents produced an average System Usability Scale (SUS) score of 70.0, which falls into the Acceptable category. These findings indicate that FMEWS is feasible as a supporting platform for flood monitoring and early warning in Buleleng Regency.
Analisis Komparatif Konfigurasi Multilayer Perceptron pada Classifier Head RoBERTa untuk Klasifikasi Ujaran Kebencian Ikhwan Habibi; Surya Agustian; Jasril Jasril; Muhammad Affandes
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.v7i2.10512

Abstract

The widespread dissemination of hate speech and offensive language on social media has increased the demand for accurate automated text classification systems. Although RoBERTaForSequenceClassification has been widely used for various for text classification task, the effect of its default classifier head configuration on classification performance has not yet been systematically evaluated. As the main contribution, this study conducts a controlled evaluation of 32 multilayer perceptron (MLP)-based classifier head configurations, varying the number of hidden layers, activation functions, and dropout rates, against the default classifier head on the English HASOC 2021 dataset for two subtasks: binary and multiclass classification. Each configuration was evaluated using Stratified 5-Fold Cross-Validation with Macro-F1 as the evaluation metric, after which the best-performing configuration was further evaluated on an independent test set. For the binary task, the best configuration achieved a test Macro-F1 of 80.90%, about 0.3 percentage points higher than the baseline's 80.59%. For the multiclass task, the configuration with the highest validation performance instead achieved a test Macro-F1 of 65.68%, about 0.4 percentage points lower than the baseline's 66.11%, showing that an advantage observed during cross-validation does not always hold on the test set. Further analysis revealed that excessively deep hidden layers combined with aggressive dimensional compression can sharply degrade performance on the multiclass task. These findings indicate that the effect of classifier head configuration is small and task-dependent, so systematic evaluation remains necessary before adopting a given configuration in place of the default classifier head when fine-tuning RoBERTa-based models.
Rancang Bangun Sistem Informasi Monitoring, Evaluasi, dan Pembinaan Tata Kelola Sekolah Dasar Menggunakan Metode Waterfall Fetrik Gutris; Muhammad Iqbal; Mia Rosmianti
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.v7i2.10517

Abstract

The implementation of monitoring, evaluation, and guidance for elementary school governance in the Regional Education Office still faces challenges in information management because data from schools has not yet been integrated, resulting in evaluation, guidance, and decision-making processes that are not yet effective. This study aims to design and develop a web-based information system to support the integrated management of monitoring, evaluation, and guidance using the Waterfall method, which includes needs analysis, design, implementation, testing, and conceptual maintenance. The research resulted in a system capable of linking the monitoring, evaluation, and guidance processes into a single operational mechanism for information management, so that monitoring results directly serve as the basis for conducting evaluations, while evaluation results serve as the basis for developing guidance without requiring reprocessing or transferring data to other media. Unlike similar systems, which generally still separate these three processes, the developed system forms a continuous information management cycle, thereby supporting data consistency and strengthening the Regional Office’s decision-making in overseeing elementary school governance. The results of Black Box Testing showed that all system functions operate in accordance with functional requirements, while User Acceptance Testing (UAT) achieved a user acceptance rate of 83%, indicating that the system is acceptable and supports users’ operational needs. Thus, this research not only produced a web-based information system but also offered an operational mechanism that integrates monitoring, evaluation, and guidance into a single information management cycle to support more effective oversight of elementary school governance.

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