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TIN: TERAPAN INFORMATIKA NUSANTARA
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27227987     DOI : -
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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 828 Documents
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Klasterisasi Pola Seismik: Perbandingan K-Means dan ST-DBSCAN pada Data Gempa Spasio-Temporal Muhammad Putra Pratama Budiman; Muhammad Zakariyah
TIN: Terapan Informatika Nusantara Vol 7 No 5 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i5.11257

Abstract

Earthquakes are natural disasters with significant socio-economic impacts; thus, understanding their distribution patterns and characteristics is essential for disaster mitigation. Indonesia is situated along the Pacific Ring of Fire with intense seismic activity, and the volume of earthquake data recorded by BMKG is too vast for manual analysis. This study compares K-Means and ST-DBSCAN using 93,815 spatio-temporal data points from a historical catalog (2008–2023) and data from the final 30 days of 2026. The number of K-Means clusters was determined using the elbow method supported by the Silhouette Score and Davies-Bouldin Index (DBI); both algorithms were evaluated using the Silhouette Score, DBI, and Adjusted Rand Index (ARI) across six tectonic zones. K-Means (k = 4) outperformed ST-DBSCAN across all three metrics (Silhouette Score: 0.1563, DBI: 2.7180, ARI: 0.4137), whereas ST-DBSCAN produced 20 clusters with a single cluster containing 70.9% of the data. Three K-Means clusters corresponded to the Sulawesi–Maluku, Sunda Arc, and Sumatra regions, while one represented a temporal artifact from the time_days feature. An ablation test excluding time_days (k = 3) improved the ARI to 0.5102. Results are presented via the Flask-based NusaSeis web dashboard with interactive maps and automated interpretations by Gemini 2.5 Flash; all 23 black box scenarios were validated. NusaSeis serves as an open seismic exploration platform enabling academics and disaster management agencies to interactively access national seismicity summaries without programming expertise, though it is not yet suitable for official tectonic zoning or hazard assessment.
Rancang Bangun Purwarupa Sistem Pewarisan Aset Digital Berbasis Dead Man's Switch Dengan Enkripsi AES-256 llham Aditya Pratama; Sutarman Sutarman; Muhammad Zakariyah
TIN: Terapan Informatika Nusantara Vol 7 No 5 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i5.11258

Abstract

Digital assets may become inaccessible to heirs when their owners die or experience emergencies. This study develops a web- and mobile-based digital inheritance prototype integrating a Dead Man’s Switch (DMS), server-side AES-256 encryption, and folder-based access control. Development followed the Waterfall model and Unified Modeling Language (UML), using Laravel for the RESTful API backend, React Native for the interface, MySQL as the database, and a cron job to check inactivity every minute. Users perform periodic check-ins; once the time limit expires, the account status changes to released and access is processed according to heirs’ permissions. Email notifications contain a download link valid for three days and a One-Time Password (OTP) for heir authentication. Black-box testing of eight functional scenarios showed that all scenarios operated as expected. Across 25 repetitions of the access-release scenario, emails arrived 4–12 seconds after the time limit was reached. Testing was limited to functionality and notification timing; load testing, penetration testing, and user evaluation were not conducted. The prototype integrates activity monitoring, data protection, and controlled cross-platform access, but does not replace a legally valid will. Further security validation and user evaluation are needed before the system is used to manage high-value digital assets.
Pengembangan Sistem PLTS Cadangan dengan Automatic Transfer Switch dan Monitoring Berbasis IoT Evi Suhaevi; Iswidodo Iswidodo; Deny Putra
TIN: Terapan Informatika Nusantara Vol 7 No 5 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i5.11259

Abstract

Solar-based backup power systems require integrated automatic source switching and electrical monitoring to support centralized operational supervision. This study aims to develop and evaluate an Internet of Things (IoT)-based photovoltaic (PV) backup power prototype incorporating an Automatic Transfer Switch (ATS). The system integrates a 30 Wp solar panel, Solar Charge Controller (SCC), 12 V 14 Ah battery, 220 VAC inverter, relay–contactor-based ATS circuit with an 11.5 VDC switching threshold, two INA219 sensors, an ESP32 microcontroller, and the Blynk IoT Platform. Evaluation comprised three days of solar panel power measurements, utility-to-PV source transfer tests, and functional assessment of remote monitoring and control with a three-second data update interval. The recorded peak panel powers were 30.03 W, 27.28 W, and 25.20 W on the respective observation days. The ATS transfer time was 1.2 s from the utility grid to the PV inverter and 1.0 s in the reverse direction. The Blynk dashboard successfully displayed voltage, current, power, estimated battery State of Charge (SOC), and ATS control status, while also receiving manual control commands. These findings demonstrate the functional integration of the prototype as an IoT-monitored PV backup power system. Further evaluation is required to establish sensor accuracy, switching transient behavior, and long-term operational reliability.
Perancangan Sistem Booking Lapangan Olahraga Basket Berbasis Web Menggunakan Metode Research and Development (R&D) dalam Meningkatkan Efisiensi Pengelolaan Lapangan Bintang Kusuma Bintang Nata; Endi Permata; Didik Aribowo
TIN: Terapan Informatika Nusantara Vol 7 No 5 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i5.11272

Abstract

Basketball court reservations are still predominantly made through conventional methods, such as phone calls, in-person visits, and private messages, which result in scheduling conflicts, double bookings, and inefficient administrative processes. This study aims to develop a web-based basketball court reservation information system to improve the efficiency of court management. This study uses the Research and Development (R&D) method with the Waterfall model, which includes requirements analysis, system design, implementation, testing, and maintenance. Data was collected through observation, structured interviews, and questionnaires. The system was developed using PHP, MySQL, HTML, CSS, and JavaScript, and is equipped with features for real-time schedule management, online booking, payment confirmation, and automated transaction reports. The results demonstrate that the website has high access speed, is responsive, and is stable for user use. Functional testing using the Black-Box Testing method showed that all system features operate as expected. After the Black-Box testing was completed, the web system was also tested for overall system speed. Based on Google PageSpeed Insights/Lighthouse testing, the web-based booking system demonstrated excellent and efficient performance across various devices. On mobile, the system achieved a performance score of 88%, an accessibility score of 93%, and a best practices score of 96%. On desktop, the system achieved a perfect performance score of 100%, with an accessibility score of 93% and a best practices score of 96%. Based on the test results, the web-based booking system was deemed highly suitable and received very positive feedback from both subject matter experts and end users. The subject matter experts’ validation of 20 indicators across eight aspects yielded an average score of 77 out of a maximum of 80, with a suitability percentage of 96.25%, which falls into the “Highly Suitable” category. Meanwhile, the results of the user response test, which involved managers and tenants, yielded an average score of 76.67 out of 80, with a 95.83% achievement rate, placing it in the “Highly Feasible” category. These results indicate that the system not only meets the criteria for technical and content validity but is also capable of providing benefits and effectively supporting operational activities and on-site booking processes.
Efektivitas Penggabungan Data Lintas Provinsi dan Augmentasi TimeGAN pada Prediksi Harga Beras Berbasis LSTM Sinta Dwi Lestari; Umar Zaky
TIN: Terapan Informatika Nusantara Vol 7 No 5 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i5.11297

Abstract

Rice price fluctuations in each province are difficult to predict, although accurate forecasts are needed to anticipate price shocks, while the multivariate data available for several provinces are often short. This study compares two ways of addressing this limitation in a Long Short-Term Memory (LSTM) model: pooling data from three provinces (East Java, Central Java, and West Java) into a single model, and adding synthetic data from a Time-series Generative Adversarial Network (TimeGAN). Monthly data (March 2024 to March 2026) from four sources (weather, rice prices, food reserve stocks, and exchange rates) yielded 25 observations per province. The 84 scenarios under three training conditions (no augmentation, 50%, and 100% augmentation) made 252 experiments, assessed with MAPE, RMSE, and MAE. The pooled model K7 was more accurate than local models in all three provinces. Without augmentation, mean MAPE fell from 2.056% to 1.258%, RMSE from IDR 326.777/kg to IDR 223.018/kg, and MAE from IDR 306.063/kg to IDR 187.795/kg. In contrast, TimeGAN augmentation produced no detectable improvement, and at 100% it tended to raise the pooled models’ MAPE from 1.541% to 1.710%. The synthetic data were easily distinguishable from the real data (mean discriminative score 0.893). For food price monitoring in regions with short data, collecting real data from other provinces appears more promising than building synthetic data, so price-forecasting systems can prioritize pooling data across provinces before adding generative models.
Evaluasi Kritis Generalisasi Multidataset pada Deteksi Hoax Berbahasa Indonesia Menggunakan BiLSTM dan IndoBERT Ria Yunita; Umar Zaky
TIN: Terapan Informatika Nusantara Vol 7 No 5 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i5.11298

Abstract

Machine learning-based Indonesian hoax detection often reports high scores on test data sharing the training data’s distribution, although such scores may misrepresent actual model capability. This critical evaluation, not a new method, analyzes how multidataset composition affects BiLSTM and IndoBERT performance and generalization, and identifies indications of shortcut learning arising from spurious correlations between labels and dataset origin. Three datasets were used: Mendeley (426 samples), Komdigi (3,951 samples), and sh1zuka (8,000 sampled), combined into 12,377 samples (8,256 Valid, 4,121 Hoax). Evaluation employed three schemes: Scheme 1 (stratified 70:15:15 split of the combined data), Scheme 2 (forward cross dataset), and Scheme 3 (reverse cross dataset). In Scheme 1, BiLSTM and IndoBERT achieved Macro F1 scores of 0.9803 and 0.9814, but these fugures cannot be read as successful hoax detection, since under cross dataset schemes Macro F1 dropped to 0.3642 and 0.5624 (Scheme 2) and 0.4231 and 0.4109 (Scheme 3), with accuracy below the majority class baseline. Dataset 1, comprising only 3.4% of the Scheme 1 test data, accounted for 87.9% of BiLSTM errors and 100% of IndoBERT errors, while the accuracy of the word “beredar” as a single label predictor fell from 96.30% on combined data to 42.96% on Dataset 1. These findings indicate that high scores under conventional evaluation reflect recognition of dataset origin more than comprehension of news content; therefore, cross dataset evaluation and source control tests should accompany random splitting on multisource data.
Aplikasi Pengingat Gamifikasi dengan Notifikasi Lokal Offline-First dan Validasi Poin Server-Side: Evaluasi Skalabilitas dan Kebergunaan Irhan Thaufa Ardhana; Afwan Anggara
TIN: Terapan Informatika Nusantara Vol 7 No 5 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i5.11326

Abstract

Procrastination is a barrier to the productivity of students and workers in the digital era, while conventional reminder applications tend to be passive and may experience notification delays due to push notification latency and mobile operating system power optimization policies. This study aims to design, implement, and evaluate DoYourTask, a hybrid client-server mobile reminder application integrating offline-first local notifications using exactAllowWhileIdle with gamification features, including experience points (XP), levels, badges, and leaderboards, supported by centralized server-side validation. Its motivational foundation draws on Self-Determination Theory (SDT), particularly the needs for competence and autonomy. Evaluation comprised Black Box functional testing using equivalence partitioning and boundary value analysis, RESTful API load testing with Apache Benchmark at 2, 5, and 10 virtual users (VU), MySQL query scalability testing with up to 10,000 task records, and a usability pilot study involving 10 respondents using the System Usability Scale (SUS). The results showed that all 21 functional test scenarios passed, yielding a 100% success rate. API testing achieved a 100% transaction success rate, a 0.00% error rate, and throughput of 2.75–3.29 requests per second, while median latency increased from 601 ms to 3,669 ms. The idx_users_xp composite index reduced inspected rows by 99% and improved leaderboard query performance by 26.9%. The mean SUS score was 80.5 ± 9.2, corresponding to Grade B and an Excellent rating. The results demonstrate the technical feasibility of the hybrid architecture and indicate good usability based on an initial evaluation.
Analisis Performa Metode BiLSTM untuk POS Tagging pada Kalimat Bahasa Indonesia Tingkat Dasar Dhafa Widy Pratama; Ledy Elsera Astrianty
TIN: Terapan Informatika Nusantara Vol 7 No 5 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i5.11343

Abstract

The effect of hyperparameters on part-of-speech tagging (POS Tagging) performance for the Indonesian language in deep learning models is rarely examined in isolation, because earlier studies usually add complex components (pretrained embeddings, CNN, CRF, or BERT), so the effect of hyperparameters on a plain Bidirectional Long Short-Term Memory (BiLSTM) cannot be isolated. The novelty of this study is a stepwise analysis of five hyperparameters (embedding dimension, hidden dimension, learning rate, train-test split ratio, and number of epochs) on a three-layer BiLSTM built from scratch in NumPy, without pretrained embeddings or CRF, on basic-level Indonesian sentences, that is, sentences commensurate with the comprehension level of elementary school (SD) students, represented by first-grade SD/MI textbooks. Secondary data comprised 10,747 sentences with 23 labels, combining the IDN Tagged Corpus and 747 textbook sentences. The effect of hyperparameters proved highly uneven: learning rate and hidden-layer capacity determined performance, whereas embedding dimension, split ratio, and number of epochs shifted accuracy by less than 0.5 percentage points. The best configuration reached 95.92% accuracy and 0.9966 weighted ROC-AUC, with labels X, UH, and FW being the hardest to predict. A five-seed test of the final configuration showed stable results (accuracy 95.82 ± 0.08% at 50 epochs and 95.76 ± 0.17% at 98 epochs; the difference between the two was not significant, p = 0.26), but small differences between hyperparameter settings have not been tested statistically. This study provides a simple and transparent baseline and a priority order for hyperparameter tuning as a starting point for NLP practitioners.

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