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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 656 Documents
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Klasifikasi Multikelas Citra Chest X-Ray Menggunakan Semi-Supervised SoftMatch pada Label Terbatas M. Nabil Dawami; Benny Sukma Negara; Muhammad Irsyad; Yusra Yusra; Febi Yanto
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9848

Abstract

Deep learning-based chest X-ray (CXR) classification frequently encounters bottlenecks due to the scarcity of labeled medical data and imbalanced class distributions. This study aims to implement a semi-supervised learning (SSL) approach utilizing the SoftMatch algorithm with a DenseNet-121 backbone for the multiclass classification of CXR images (Normal, COVID-19, and Pneumonia) under limited label conditions. SoftMatch is specifically selected for its capability to mitigate the quantity-quality trade-off through an adaptive pseudo-label soft-weighting mechanism. A dataset comprising 5,228 images is allocated via a stratified split into 70% training data, 10% validation data, and 20% testing data. Experiments are conducted across three labeled data proportion scenarios (5%, 10%, and 20%), each evaluated with and without Uniform Alignment. Evaluation metrics include accuracy, macro F1-score, confusion matrix, ROC-AUC, supported by visual interpretability analysis using Grad-CAM. The experimental results demonstrate that the model remains robust under the most critical scenario (5% labels), achieving an accuracy of 91.68% and a macro F1-score of 91.72% when integrating Uniform Alignment (UA), outperforming the scenario without UA, which records an accuracy of 90.73% and a macro F1-score of 90.82%. The best performance for the UA configuration is achieved in the 10% label scenario (accuracy 94.46%; macro F1-score 94.58%), while the peak overall performance is attained by the 20% label scenario without UA (accuracy 95.79%; macro F1-score 95.89%). These findings indicate that Uniform Alignment is effective in low-to-medium label conditions but does not consistently enhance performance at higher label proportions.
Perbandingan Kinerja Support Vector Machine (SVM) dan K-Nearest Neighbor (KNN) dalam Klasifikasi Stunting Salma Fathiyatur Rizky Munir; Yisti Vita Via; Eka Prakarsa Mandyartha
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9868

Abstract

The evaluation and comparison of the performance of the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms are the primary objectives of this study, particularly in classifying stunting conditions among toddlers. The dataset in this study consists of primary data obtained from the Patianrowo Community Health Center in Nganjuk Regency, with an initial total of 1,102 data points on children aged 0–60 months. After data cleaning, missing values were removed, reducing the dataset to 1,067 data points. Subsequently, the data was divided into 853 training data points and 214 test data points using the train-test split method with an 80:20 ratio. The preprocessing stage included removing missing data, transforming labels into numerical form, and normalizing the data using the min-max scaling method to standardize the feature value ranges. To evaluate the model, a confusion matrix was used with the metrics of accuracy, precision, recall, and F1-score. The test results showed that the KNN algorithm with K=5 produced an accuracy of 96.72%, precision of 91.25%, recall of 67.73%, and an F1-score of 77.52%. Meanwhile, the polynomial SVM algorithm demonstrated improved performance with an accuracy of 97.47%, precision of 90.82%, recall of 78.96%, and an F1-score of 82.55%. Based on these results, SVM is considered more effective in classifying stunting in the dataset used.
Evaluasi Montreal Forced Aligner dan Goodness of Pronunciation untuk Penilaian Pelafalan Bahasa Sunda Abdul Fatahillah; Sigit Puspito Wigati Jarot
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9869

Abstract

Sundanese is the second most widely spoken regional language in Indonesia, yet automated pronunciation assessment systems for this language remain extremely scarce. This study presents a systematic evaluation of the Montreal Forced Aligner (MFA) and Goodness of Pronunciation (GOP) pipeline for Sundanese pronunciation assessment within a prototype voice-based learning application. The dataset comprises 2,500 valid utterance samples collected from 50 native Sundanese speakers, covering 10 basa loma vocabulary items spanning 20 unique phonemes. MFA evaluation revealed total and systemic alignment failure: all 2,500 files (100%) were identified as problematic, with 17 of 20 phonemes consistently assigned exactly 10-millisecond durations. Three distinct parameter configurations produced identical failure rates (100%), confirming that the failures are intrinsic to MFA's limitations with very short-duration single-word audio (mean 0.69 seconds) for low-resource languages. GOP evaluation yielded a global top-1 accuracy of only 26.1%, characterized by anomalous dominance of the /l/ phoneme as top-1 for 14 of 20 phonemes. Functional testing demonstrated the system's inability to discriminate correct from incorrect utterances. On the technical side, the React Native and FastAPI prototype application was successfully implemented, with 6 of 8 black-box test scenarios passing. This research provides three principal contributions: (1) empirical contribution in the form of the first quantitative evidence that the standard MFA-GOP pipeline cannot be directly applied to Sundanese as a low-resource language with short-duration single-word audio; (2) methodological contribution in the form of an empirical baseline and replicable evaluation framework applicable to other regional languages of Indonesia; and (3) practical contribution in the form of a React Native–FastAPI client-server prototype that serves as a starting point for further development of Sundanese pronunciation assessment systems using alternative approaches.
Analisis Komparatif MLP dan GraphSAGE dalam Deteksi Bot Twitter/X pada Benchmark TwiBot-22 Mochammad Fikri Chaerul Chalik Ramdhan; Sigit Puspito Wigati Jarot
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9891

Abstract

Bot accounts on Twitter/X remain a significant challenge because they affect information integrity, distort public discourse, and complicate platform moderation. This article evaluates two bot detection approaches on the TwiBot-22 benchmark: a profile-feature-based Multilayer Perceptron (MLP) and a graph-based GraphSAGE model, using a 12-Stage Evaluation Framework that covers data validation, feature engineering, model training, threshold analysis, feature ablation, and multi-seed evaluation. The study is limited to an offline benchmark setting with 1,000,000 labeled accounts, 13.99% bots and 86.01% humans, and a fixed split of 70% training, 20% validation, and 10% testing. In the single-seed 15-feature comparison, MLP achieved F1(bot) of 0.53 and PR-AUC of 0.48, while GraphSAGE reached F1(bot) of 0.53 and PR-AUC of 0.46. In the confirmatory three-seed evaluation, the user_only_8 configuration produced F1(bot) of 0.53 and PR-AUC of 0.49 with lower variance, whereas all_15 produced F1(bot) of 0.53 and PR-AUC of 0.47 with higher variance. These findings indicate that the more economical profile-only configuration preserves practically identical binary-decision quality, offers better probability ranking quality, and shows lower variance. The main contribution of this article is a feature-economy argument: on TwiBot-22, added graph and feature complexity does not automatically yield proportionate practical gains.
Integrasi Algoritma Cosine Similarity pada Sistem Informasi LPPM untuk Otomatisasi Deteksi Redundansi Judul Jeffadha Rhenggajati Umbaratama; Wakhid Kurniawan
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9902

Abstract

Research title duplication is a crucial problem that frequently occurs in the academic environment, particularly in the governance of the Institute for Research and Community Service (LPPM). The current manual title verification process is considered inefficient, time-consuming, and highly prone to human error, potentially allowing proposals with substantial similarity to pass. This study aims to design and develop a web-based LPPM Information System integrated with a smart automatic title similarity detection feature for a case study at Universitas Muhammadiyah Karanganyar. The proposed solution is the development of a distributed system using a microservices architecture, integrating a Laravel framework web service and a Python (FastAPI) Natural Language Processing (NLP) computational service via REST API communication protocol. The software development method adopts Agile Development to accommodate requirement changes iteratively. The primary contribution of this research is the creation of an academic validation instrument based on a distributed architecture that isolates the text computational workload from the main server. The application of the Cosine Similarity algorithm combined with Term Frequency-Inverse Document Frequency (TF-IDF) weighting and the Sastrawi lexical library is used to calculate text similarity precisely. Black Box functional testing using the Equivalence Partitioning technique shows that the API integration runs without errors. Given the research focus on system integration, the algorithm evaluation was conducted using a controlled synthetic dataset simulating various text manipulation techniques. The test results show that the system can precisely identify basic syntactic modifications. However, the system transparently acknowledges a computational limitation (a drop in detection to 12.74%) in recognizing semantic similarities purely based on synonyms. The implementation of this technology is expected to help LPPM administrators validate the originality of proposals efficiently, objectively, and in real-time.
Evaluasi Usability pada Website KPU Menggunakan Metode System Usability Scale Rusmawan Abdullah Sani
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.9914

Abstract

The digitalization of public services at the General Elections Commission (KPU) is vital for election transparency, yet technical obstacles such as slow loading times and navigation complexity are still frequently reported. This study aims to evaluate the usability of the kpu.go.id website using the System Usability Scale (SUS) method to measure user perception of satisfaction, effectiveness, and efficiency in a standardized manner. This study was conducted in Indonesia with 100 respondents who had previously accessed the kpu.go.id website, using accidental sampling. A quantitative descriptive approach was employed by distributing a questionnaire consisting of ten standard SUS statements. The results show a mean SUS score of 71.5. Based on evaluation parameters, this score places the KPU website in the "Marginal High" category for acceptability, with a "Good" adjective rating and a "B" grade scale. Analysis based on Nielsen's usability dimensions reveals that Satisfaction (81.75) and Memorability (81) perform best, while the Errors (66) and Learnability (66.25) dimensions record the lowest scores. These findings indicate that while the website is functional and acceptable, significant optimization is required in error handling and navigation intuitiveness to bridge the digital divide and ensure service inclusivity for all segments of society. These results serve as a data-driven recommendation for the KPU to enhance its user interface to bolster public trust in democratic information systems.
Perbandingan Efisiensi Waktu Pemrosesan Cuti Konvensional dan Digital Berbasis Web Terintegrasi Notifikasi Telegram Nabil Abiyu Nawwar; Wakhid Kurniawan
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9916

Abstract

The administrative process of personnel leave applications in various higher education institutions is currently predominantly managed conventionally using physical forms. This manual approach frequently triggers various operational problems, such as processing time inefficiency, a high risk of misplaced documents, and approval delays caused by leaders not always being at the workplace. Although some institutions have transitioned to web-based digital leave systems, these systems are still considered ineffective because users are required to log in repeatedly just to check for status updates. Based on these conditions, a crucial research gap is identified. To date, no studies have focused on the addition of the Telegram Bot notification feature as a real-time solution while simultaneously conducting an empirical and quantitative comparison of processing time efficiency between manual governance and the digital system. To fill this gap, this study aims to design and develop a web-based leave application information system integrated with the Telegram Bot API and to measure the significance of its processing time efficiency. As a technological solution, this system utilizes the Webhook algorithm, which is capable of sending instant approval notifications directly to leaders' mobile devices. In its implementation, this study applies a quantitative experimental approach involving 30 testing scenario samples. The software's feasibility was first validated through the Black Box Testing method to ensure its functionalities operate correctly. Afterward, it was followed by comparative data analysis using the parametric statistical Paired Sample t-Test assisted by SPSS software. The test results show that all system modules have operated validly. Furthermore, quantitative measurement proves that the implementation of the system with Telegram notifications can drastically reduce the bureaucratic flow, lowering the average processing time from 370.33 minutes in the manual method to only 1.33 minutes. Statistical tests confirm that this efficiency difference is highly significant (Sig. 2-tailed = 0.000 < 0.05). The main contribution of this research is providing empirical evidence regarding operational time savings of more than 95%, as well as presenting a practical solution framework for institutions in adopting automation technology to achieve a modern, transparent, and responsive administrative governance.
Evaluasi Kualitas Website SEGO PETANI terhadap Kepuasan Pengguna Menggunakan WebQual 4.0 Desi Lajar Sari; Nova Tri Romadloni
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.9940

Abstract

This study aims to evaluate the quality of the SEGO PETANI website using the WebQual 4.0 method and analyze its effect on user satisfaction. The SEGO PETANI website is a digital platform designed to help farmers report pest and plant disease attacks in order to support the effectiveness of technology-based agricultural services. The research was conducted at Dinas Pertanian Pangan dan Perikanan Kabupaten Karanganyar involving 167 respondents who had used the SEGO PETANI website. This study employed a quantitative approach with a descriptive-verificative design and purposive sampling technique. Data were collected through questionnaires based on a five-point Likert scale developed from the dimensions of usability quality, information quality, and service interaction quality. Data analysis was performed using SPSS through validity testing, reliability testing, multiple linear regression, t-test, F-test, and coefficient of determination analysis. The results showed that the website quality was categorized as good, with average scores of 4.00 for usability quality, 4.03 for information quality, and 4.06 for service interaction quality. Website quality significantly affected user satisfaction with a coefficient of determination value of 71.1%. Service interaction quality was identified as the most dominant factor influencing user satisfaction. This study provides practical contributions as an evaluation material for improving digital agricultural services and academic contributions through the implementation of the WebQual 4.0 method in public service-based information systems.
Implementasi Sistem Informasi Penjualan Mebel Berbasis Web Menggunakan Metode SCRUM Bernandika Reyhan Groovytala; Galet Guntoro Setiaji; Ahmad Rifa'i
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.9947

Abstract

This study focuses on the design and implementation of a web-based furniture sales application at UD. Andhireyma, Boyolali Regency, Central Java Province. The rationale for developing this software stems from the urgent need to optimize efficiency in daily transaction management, product inventory updates, and sales reporting. Involving the active participation of 14 respondents, including business owners, operational staff, and customers, this study adopted the Scrum framework. The methodology’s phases—product backlog, sprint planning, sprint review, and sprint retrospective—were implemented to ensure that system development iterations could dynamically adapt to user expectations. From a technical perspective, the application architecture was built using the PHP programming language and MySQL database management, integrated with HTML, CSS, and JavaScript elements. Comprehensive data collection was conducted through a series of field observations, continuous documentation, and in-depth interviews. Furthermore, to ensure the overall functionality of all features, the Black Box Testing method was strictly applied. Evaluation findings indicate that the implementation of this digital system successfully accelerated and structured the management of commodity data as well as transaction history reports. The risk of manual recording errors was significantly reduced, which directly corresponds to an improvement in the quality and effectiveness of customer service. Based on the successful functional test results, which recorded a success rate of 80%, this e-commerce platform is recommended and deemed highly suitable for immediate implementation in UD. Andhireyma business operations.
Implementasi Retrieval Augmented Generation dan Dynamic Topic Modeling untuk Smart Assistant Berbasis Web Graciella Eunike Bawiling; Fify Mustika Wondal; Maksy Sendiang; Tracy Kereh
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.9951

Abstract

Center for research and Community Service (P3M) Politeknik Negeri Manado faces challenges in providing information services that are fast, accurate, and relevant to the academic community. The research title consultation process is still carried out manually and has not been supported by a system that is able to map the latest research trends to provide prospective research topic recommendations. This condition has the potential to cause delays in information and lack of updates on global research developments for lecturers and students. This study aims to develop a web-based Smart Assistant feature that is able to automate P3M information services while providing research title recommendations based on research Trend Analysis. The system was developed by integrating two Artificial Intelligence methods, namely Retrieval Augmented Generation (RAG) to generate chatbot answers based on P3M internal documents and external data through APIs, and Dynamic Topic Modeling using a topical algorithm to analyze research title trends from scientific publication data. The results of the study in the form of Smart Assistant features P3M Manado State Polytechnic, which provides interactive conversation Services and research title recommendations. This system is expected to improve the efficiency of administrative services and help lecturers and students in determining relevant research topics and based on the latest data.

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