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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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Habit Tracker Application Using the Gamification Method to Improve Student Discipline Mirnawati Mirnawati; Riris Septiana Sita Dewi
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.v7i5.11069

Abstract

Student discipline in academic activities remains a continual challenge, commonly due to inadequate time management and frequent electronic distractions. Existing Habit Tracker applications typically focus on recording activities, with limited capacity to sustain user motivation. This study describes the design, development, and evaluation of an Android-based Habit Tracker application that incorporates seven gamification elements: points, levels, badges, streaks, leaderboards, rewards, and reminders. The application seeks to improve student discipline at Universitas Muhammadiyah Banten. This study contributes a Habit Tracker that integrates seven gamification elements in a single application aimed at academic discipline, together with empirical evidence on its user acceptance and on changes in student discipline scores. Employing a quantitative One Group Pretest-Posttest design and the Prototype development method, data were collected from 30 students across the 15th, 16th, and 17th cohorts using pre-test, post-test, and User Acceptance Test (UAT) questionnaires on a Likert scale. Data analysis included validity testing (Corrected Item-Total Correlation), reliability testing (Cronbach's Alpha), and a Paired Sample T-Test to compare discipline scores before and after application use. The UAT indicated a user acceptance rate of 75.37%, categorized as good. The Paired Sample T-Test demonstrated an increase in mean discipline scores from 31.53 to 38.27, with a t-value of -11.914 and a significance value of 0.000 (p<0.05), leading to the rejection of H0 and acceptance of H1. The results indicate that the gamification-based Habit Tracker application developed in this study is associated with a significant increase in student discipline scores at Universitas Muhammadiyah Banten.
Analisis Efektivitas Pembobotan TF-IDF dan Metode Cosine Similarity Untuk Deteksi Plagiarisme Rido Anugrah; Atang Susila
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.v7i5.11135

Abstract

Plagiarism in academic documents undermines scientific integrity and significantly degrades the quality of education. This study aims to analyze the effectiveness of combining Term Frequency-Inverse Document Frequency (TF-IDF) statistical weighting with the Cosine Similarity distance metric for detecting the degree of global semantic similarity at the document level. The computational experiments were conducted using the Python programming language, supported by the Natural Language Toolkit (NLTK) and Scikit-Learn libraries. The test dataset consists of an internationally standardized secondary dataset from the PAN at CLEF 2011 corpus (a subset of the PAN-PC-2011 corpus), comprising 500 pairs of suspected (query) documents and their corresponding source documents. As the test dataset contains only positive pairs (instances of plagiarism) and no negative pairs (non-plagiarized instances), the binary classification evaluation focuses on True Positives (TP), False Negatives (FN), positive-class accuracy, and recall. The primary contribution of this research is the provision of empirical evidence regarding an optimal threshold (2%) and a comprehensive analysis of the impact of "vector dilution" in full-length documents. The highest recall achieved in the tested scenarios was 94% (470 TP out of 500 pairs) at a 2% threshold. Recall decreased as the threshold increased, reaching 0% at thresholds of 80% and 90%. These results indicate that the threshold significantly influences detection sensitivity for positive instances; however, it does not yet reflect the system's ability to correctly reject non-plagiarized documents. Consequently, this method is best positioned as an initial screening tool that requires further evaluation using both positive and negative pairs.
Analisis Komparatif C4.5, XGBoost, dan CatBoost Berbasis SMOTE untuk Prediksi Kelayakan Calon Karyawan Putri Suci Ramdani; Galih Hendro Martono; Moch. Syahrir
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.v7i5.11137

Abstract

Recruitment is an important stage in helping organizations identify candidates who meet job requirements. Manual assessment of a large number of candidates can be time-consuming and may lead to inconsistent decisions. This study aims to compare the performance of C4.5, XGBoost, and CatBoost in classifying candidate eligibility and to determine the model with the most consistent performance. The AI Fair Recruitment Dataset was used, consisting of 119,971 candidate records. The study followed the CRISP-DM framework, including Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. Data preparation included attribute selection, encoding, a stratified split of 90% training data and 10% test data, and class imbalance handling using SMOTE applied only to the training data. The models were compared using accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrix. The results show that XGBoost and CatBoost achieved 98% accuracy, while C4.5 achieved 95%. CatBoost achieved 96% precision, 98% recall, 98% F1-score, and 97% ROC-AUC, providing the most consistent performance among the three models. The confusion matrix also shows that CatBoost produced the lowest classification errors, with 121 False Positive and 50 False Negative cases. These results indicate that CatBoost was the most suitable model under the experimental setting of this study. The model was subsequently implemented in a Flask-based prototype as an initial screening support tool. This study did not quantitatively evaluate algorithmic fairness; therefore, high predictive performance should not be interpreted as evidence that the model is bias-free.
Penerapan Metode Reorder Point pada Sistem Kasir dalam Menentukan Waktu Pengadaan Barang Andhika Tri Hazra; Adityo Permana Wibowo
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.v7i5.11229

Abstract

Toko Kardus Mitra Packing still manages transactions and inventory conventionally, resulting in obstacles such as the lack of real-time stock deduction, slow cashier services, and the determination of procurement times based solely on subjective estimates, which triggers an average stockout frequency of 5 to 8 times per month for primary packaging products and a 42% cashier service delay during peak hours. If not addressed immediately, this condition will result in the loss of sales potential and disrupt logistics availability, especially during order surges from customers. The purpose of this study is to develop a mobile and web-based cashier system by implementing the Reorder Point (ROP) method as a solution for automatically determining procurement times. The primary contribution and novelty of this research lie in the real-time architectural integration between a front-line mobile Point of Sale application and a server-side probabilistic Reorder Point computation engine, enabling every sales transaction to dynamically recalculate daily demand standard deviation and safety stock thresholds without separate manual analysis. This system was built using the Flutter framework for employee operations, Laravel for the owner's dashboard, and a MySQL database. Functionality testing was conducted using the Black Box Testing method. The test results showed that the system functioned with a 100% success rate in all test scenarios and reduced transaction processing time by 68.5% to an average of 55 seconds per transaction. The final result of this research is a cashier system capable of automating daily transactions, synchronizing inventory in real-time, and mathematically calculating safe stock limits to provide early procurement warnings on the dashboard, thereby significantly facilitating the store owner in making more accurate and data-driven restocking decisions.
Tren Teknologi dan Mikrokontroler pada Sistem Pakan Ikan Otomatis Berbasis IoT: Tinjauan Sistematis 2022–2026 Faiz Abdurrahman Mustofa; Dani Sasmoko; Sumaryanto Sumaryanto
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.v7i5.11231

Abstract

This study aims to analyze technology trends and microcontrollers in automatic fish feeding systems based on the Internet of Things (IoT) through a Systematic Literature Review (SLR) of publications from 2022–2026. The method used is SLR with the PRISMA 2020 protocol, bibliometric analysis assisted by VOSviewer, and content analysis. Data were taken from the ScienceDirect database, with an initial search yielding 200 articles for Research Question 1 (RQ1) and 50 articles for RQ2. After identification, screening, and eligibility assessment, 32 articles for RQ1 and 17 articles for RQ2 were included for further analysis. The results show a shift from simple timer-based systems toward intelligent architectures integrated with AI and machine learning, with IoT+AI dominating at 35%. The Arduino platform dominates at 60%, followed by ESP32/NodeMCU, which is increasing due to wireless connectivity. The most critical sensors are temperature (30%), pH (25%), and dissolved oxygen (25%), while feeder actuators dominate at 30%. Publications peaked in 2025 with 26 articles. For RQ2, Arduino family microcontrollers (Uno, Mega, Nano) are the most dominant, covering two-thirds of articles, but there is a gradual shift toward ESP32 and ESP8266. An important finding reveals a reporting gap where 70% of articles do not explicitly list hardware specifications, hindering research replication and technology adoption. This study provides a current technology trend map and evidence-based recommendations for researchers and farmers in selecting the most mature technology for sustainable automatic feeding systems in the aquaculture sector.
Implementasi Sistem WhatsApp Blast Berbasis Web dengan Fitur Segmentasi Calon Mahasiswa Menggunakan K-Means Clustering Dilo Elvonsyah; Denny Ganjar Purnama
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.v7i5.11246

Abstract

The delivery of prospective student admission information through WhatsApp requires structured contact management and target-group selection. This study develops a web-based WhatsApp Blast system with prospective student segmentation using K-Means Clustering. The system uses React and Next.js, Node.js and Express, PostgreSQL, a Python preprocessing and clustering service, and REST APIs. The clustering variables are study-program interest, school of origin, school major, and domicile; names and WhatsApp numbers are retained only as identity attributes. Actual testing used 120 controlled synthetic records. The system stored 113 valid records, detected 3 duplicates and 4 invalid records, and recorded one missing value in each clustering attribute. All missing values were imputed as Tidak Diketahui, so all 113 valid records entered clustering with no exclusion. ILKOM was normalized to Ilmu Komunikasi, and One-Hot Encoding produced 35 features. Evaluation for K=2–6 selected K=5 through agreement among Elbow, Silhouette, and Davies-Bouldin. The K-Means run produced an SSE of 300.1250, a Silhouette Score of 0.1110, a Davies-Bouldin Index of 2.6207, and cluster sizes of 30, 27, 20, 19, and 17. All thirteen executed Black Box scenarios passed. These results validate the implementation on synthetic data and do not generalize the prospective-student population.
Chatbot Admisi Berbasis LLM dengan Retrieval-Augmented Generation, Ekstraksi Data Prospek, dan Dashboard Analitik Kamal Abrar Ramadhan; Augury El Rayeb
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.v7i5.11247

Abstract

New-student admission services face high inquiry volumes during enrollment periods and the limitations of rule-based chatbots in handling natural-language variation. This study develops and audits an admission chatbot based on a large language model that augments each request with the entire Firestore FAQ collection (non-selective full-context augmentation rather than ranked retrieval), supported by prospect-data capture, user feedback, an analytics dashboard, and Gemini-assisted knowledge-base scraping. The audit covers the Flask backend, test files, the production Next.js frontend bundle, and read-only Firestore aggregations that do not expose personal data. A 28 September 2026 snapshot recorded 238 FAQs, 191 chat logs, 9 leads, and 14 feedback records; 9 were helpful and 5 unhelpful, yielding a 64.3% helpfulness rate. The earlier 8/8 result is retained only as a test-dataset result rather than the production knowledge-base size. The average Heuristic Lexical Relevance Score of 0.54 and the Lexical Grounding Indicator range of 0.27–0.80 were derived only from illustrative text and are not treated as validated semantic or hallucination-detection measures. An internal Cache Microbenchmark recorded a 99.2% reduction in serialization overhead rather than end-to-end latency, while the previously reported 90% cache hit rate could not be reproduced. The initial audit reported a suspected static administrative token in the frontend bundle; the developer stated that dashboard access uses Firebase Authentication for registered accounts. Token classification and backend authorization still require code verification. Additional public-interface testing produced relevant responses in four UPJ-related cases and refusals in two out-of-domain cases, limited to six purposively selected cases. Prospect conversion and CSAT cannot yet be calculated because the funnel denominator and a CSAT instrument are unavailable. The main contribution is an implementation-grounded framework that separates production observations, test-dataset results, heuristic proxies, and outcomes still requiring experiments.
Implementasi Contextual Multi-Armed Bandit dengan Algoritma LinUCB untuk Rekomendasi Template pada Sistem Kampanye WhatsApp Berbasis Web Reigan Chenartha; Denny Ganjar Purnama
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.v7i5.11248

Abstract

Uniform WhatsApp campaign templates may overlook differences in timing and audience characteristics. This study develops an adaptive and auditable template recommendation module for a web-based campaign system. Design Science Research Methodology guides problem identification, objective definition, artifact development, demonstration, and evaluation. The disjoint Linear Upper Confidence Bound (LinUCB) algorithm treats Informative, Persuasive, and Urgency templates as three available actions. A 19-dimensional context vector encodes day, time bucket, audience, and a bias term. Template selection maximizes a linear reward estimate plus an exploration bonus, while the selected action is updated using delivered, read, and replied rewards weighted at 0.2, 0.3, and 0.5. Synthetic evaluation comprises 30 trials of 100 contexts each, with an exploration parameter of 0.3 and parent seed 1790543727863. LinUCB achieves a mean total reward of 39.520 with a standard deviation of 0.631, compared with 36.800 and 1.790 for the context-blind baseline. The mean improvement is 7.39%, with a mean regret of 2.769. The contribution is an integrated context–decision–feedback workflow with decision logging, operator control, and repeated evaluation isolated from operational data. These findings support artifact feasibility in the synthetic environment; improvements in real recipient responses require field validation.
Integrasi Large Language Model dengan FAQ Retrieval dan Sinkronisasi Basis Pengetahuan Berbasis Web Scraping pada Chatbot Layanan Admisi Rizky Nathaniel Lukas; Cahyono Budy Santoso
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.v7i5.11249

Abstract

University admissions services require timely and consistent answers, whereas manually maintained chatbot knowledge bases can quickly become outdated. This study develops an admissions chatbot integrating a Large Language Model, semantic FAQ retrieval, and knowledge base synchronization through web scraping. The system combines a Next.js interface, a Flask backend, Firebase Firestore, BeautifulSoup for static pages, Selenium for dynamic pages, and a generative model constrained by retrieved FAQ context. Development follows Design Science Research. Retrieval represents each FAQ as a 384-dimensional multilingual embedding, measures similarity using cosine similarity, and passes the top five FAQs to the model as answer context. Semantic retrieval is compared with a TF-IDF baseline, while answer quality is compared with an LLM-only configuration. Because operational data were unavailable, the evaluation used a controlled synthetic benchmark comprising 180 questions and 30 test URLs. In the simulation, semantic retrieval achieved 88.1% Hit@1, 93.8% Hit@3, and 0.909 MRR. FAQ-grounded generation improved simulated answer correctness from 72.2% for the LLM-only configuration to 91.1% and reduced unsupported answers from 17.8% to 5.0%. Direct functional testing in the deployed environment showed that four of seven scenarios passed. These findings indicate design feasibility, but field validation with real users remains necessary. The contribution of this study is an auditable orchestration design that connects official-source scraping, administrator curation, semantic retrieval, and the LLM in a single workflow, together with a separated evaluation framework for retrieval, answer quality, scraped-data quality, and system performance.
Sistem Deteksi dan Monitoring Terpusat Penyakit Daun Padi Berbasis Mobile Menggunakan MobileNetV2 Yogi Pranoto; 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.v7i5.11256

Abstract

Rice leaf diseases such as Bacterial Leaf Blight, Brown Spot, and Leaf Smut are a major threat to agricultural productivity in Indonesia because manual identification by farmers remains subjective, slow, and error prone. This study aims to develop a rice leaf disease detection system that integrates a Flutter based mobile application with a web based monitoring dashboard, so that diagnosis can be performed independently by farmers while being monitored centrally by system administrators. The method applied is a Convolutional Neural Network with a transfer learning approach on the MobileNetV2 architecture, trained using an expanded dataset of 300 rice leaf images obtained from primary field observation data in Klaten and secondary data from the Kaggle platform, then converted to TensorFlow Lite format to enable on-device inference on mobile devices. The system backend was built using Flask and a MySQL database to manage user data and detection history. Experiments on four hyperparameter scenarios showed that a configuration of 20 epochs, batch size 32, and learning rate 0.001 produced the best performance with a test accuracy of 95.24 percent and a loss of 0.1506 without significant indication of overfitting. The final model size of 9.71 MB with an average inference time of under 2 seconds proves that this system is feasible to be applied as an accurate, lightweight, and responsive rice disease diagnosis tool in the field.

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