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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 656 Documents
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Sistem Informasi Manajemen Laundry Berbasis Web untuk Optimalisasi Operasional Usaha Menggunakan Metode Waterfall Aditya Tazkia Aulia Mufid; Apriade Voutama
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.v6i12.9568

Abstract

The operational management of micro, small, and medium-sized laundry businesses that still rely on manual recording methods constitutes a fundamental problem affecting operational efficiency, data accuracy, and service quality. Freelance Laundry, as the research subject, faces challenges including transaction recording errors, difficulty in tracing customer data, and delays in compiling operational reports. This study aims to design and implement a web-based laundry management information system capable of optimizing the management of transaction data, customer information, service packages, and financial reports within a single integrated platform. System development was carried out using the Waterfall method, comprising the stages of requirements analysis, design, implementation, testing, and maintenance. The system design employed Unified Modeling Language (UML), including use case diagrams and Entity Relationship Diagrams (ERD). The system was developed with three user roles admin, cashier, and owner each with distinct functions and access rights. Testing was conducted using the Black Box Testing method across 15 test scenarios covering all functional modules of the system. The test results demonstrated a 100% success rate across all tested features, including user authentication, customer data management, transaction processing, and financial report export. This system has proven capable of reducing manual recording errors, accelerating administrative processes, and improving the accuracy of operational data management at Freelance Laundry.
Analisis Sentimen Ulasan Pengguna QRIS pada Aplikasi GoPay: Studi Komparatif Algoritma Support Vector Machine dan Decision Tree Berbasis TF–IDF Erlinda Sistia Aritonang; Yuwan Jumaryadi
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.v6i12.9582

Abstract

This study aims to analyze user sentiment towards the QRIS feature in the GoPay application based on reviews on the Google Play Store and to build a sentiment classification model using a machine learning approach. A total of 20,746 reviews were collected and filtered using QRIS-related keywords, resulting in 4,347 relevant reviews. The data were manually labeled and preprocessed, then extracted using the TF–IDF method. The analysis results show a sentiment distribution consisting of 49.49% positive, 35.34% negative, and 15.18% neutral. The classification process was carried out using the Support Vector Machine (SVM) and Decision Tree algorithms. The evaluation results showed that Decision Tree achieved 79% accuracy with precision, recall, and F1-score values ​​of 79% each, while SVM produced 78% accuracy with precision of 79%, recall of 78%, and F1-score of 78%. The difference in performance between the two models was relatively small, so both had equal capabilities in sentiment classification, although Decision Tree showed slightly better metric consistency.
Analisis Performa YOLOv8 dan Marker Clustering pada Sistem Terintegrasi Deteksi Dini Hama Padi dan Pengaduan Petani Berbasis Mobile dan Web Melody Putri Salzabila Gigir; Aksai Saputra; Harson Kapoh; Maksy Sendiang; Anthoinete Waroh
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.v6i12.9597

Abstract

The escalation of pest attacks on rice commodities is a serious threat that requires immediate mitigation response. The main problem found in the field is the slow flow of information due to a manual reporting system that is fragmented between farmers and the agricultural department administration. This study aims to design an integrated geographic information system that combines object detection capabilities based on artificial intelligence on mobile devices with a web-based complaint management dashboard. The research location was conducted at the North Sulawesi Protection Center for Food Crops and Horticulture (BPPMTPH). The method used was Prototyping with a focus on modeling spatial data flows and system functionality testing analysis techniques. The proposed technical solution implements the You Only Look Once (YOLO) algorithm through a cloud-based application programming interface for automatic pest classification. The results of the study showed that the built client-server architecture was able to integrate geographic labeling features accurately. The implementation of the Marker Clustering method on the web side proved effective in simplifying the visualization of dense pest distribution data into systematic information. This system successfully eliminated the need for manual location authentication, allowing damage metrics and field coordinate points to be distributed to the central database instantly.Data alignment between visual reports and statistics of this area provides strong support for authorities in making more responsive pest control decisions.
Evaluasi Kinerja Algoritma Naïve Bayes, SVM, dan IndoBERT pada Analisis Sentimen Ulasan Pengguna Gojek Berbasis Text Mining I Wayan Aries Agetia; Ni Luh Eka Armoni; I Putu Ari Utama Irawan
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.v7i1.9617

Abstract

This study aims to evaluate and compare the performance of Naïve Bayes, Support Vector Machine (SVM), and IndoBERT algorithms in the task of sentiment classification of user reviews on the Gojek application. Data were collected through web scraping from the Google Play Store and subsequently labeled into three sentiment categories: negative, neutral, and positive. A quantitative approach with a descriptive-comparative design was employed. The research procedure consisted of data collection, text preprocessing, dataset splitting into training and testing sets, model development, and evaluation using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results indicate that the IndoBERT algorithm achieved the best performance, with an accuracy of 92.46%, outperforming Naïve Bayes (87.94%) and SVM (86.43%). Furthermore, IndoBERT demonstrated greater consistency in precision, recall, and F1-score across all sentiment categories. In contrast, Naïve Bayes exhibited a tendency to misclassify certain classes, while SVM showed relatively stable performance, although it did not reach optimal results. These findings suggest that transformer-based approaches are more effective in capturing the contextual complexity of the Indonesian language. This study contributes by providing a comparative analysis of classical and transformer-based methods in Indonesian sentiment classification and offers empirical evidence of the superiority of transformer-based approaches in capturing linguistic contextual nuances in user reviews of digital applications.
Implementation of an Automatic Waste Sorting System using YOLOv5s with TFLite Conversion on Raspberry Pi Jefrian Arya Hernanda; Kurniawan Dwi Irianto
TIN: Terapan Informatika Nusantara Vol 6 No 11 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i11.9669

Abstract

Waste management remains a major environmental challenge in Indonesia, particularly due to the low level of public awareness in sorting waste based on its type. This research aims to design and implement an image-based automatic waste sorting system using the YOLOv5s algorithm with TensorFlow Lite conversion on a Raspberry Pi 3B+. The research was conducted through a system development approach without involving human respondents, focusing on performance evaluation using an image dataset consisting of three categories: paper and tissue, plastic bottles, and cans. The proposed system integrates hardware components, including a camera, servo motors, an ultrasonic sensor, and an LCD, with software components such as YOLOv5s, OpenCV, and TensorFlow Lite. The model performance was evaluated using precision, recall, and mean Average Precision (mAP), while system functionality was assessed through hardware testing. The results show that the model achieved a precision of 0.986, recall of 0.978, and mAP@0.5 of 0.99, indicating excellent detection performance. In addition, the implementation of TensorFlow Lite significantly improved computational efficiency, with the system achieving a processing speed of 173.9 frames per second (FPS). These results demonstrate that the proposed system is capable of performing accurate and efficient real-time waste classification on resource-constrained devices. This research contributes by providing an efficient and practical implementation of real-time waste sorting using a lightweight deep learning model on embedded hardware.
Analisis Klasifikasi Sentimen Neobank: Perbandingan Konfigurasi N-Gram pada TF-IDF Menggunakan Naive Bayes dan SVM Fatha Amin Mujtahid; Badroe Zaman; Galet Guntoro Aji
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.v6i12.9702

Abstract

The increasing number of Neobank users in Indonesia has led to a growth in user reviews on the Google Play Store, which can be utilized to assess service satisfaction and user experience. Manual analysis of these reviews is inefficient, prompting the use of automated machine learning approaches. This study evaluates the effect of N-Gram configurations in TF-IDF feature extraction on the performance of sentiment classification of Neobank reviews using Naive Bayes (NB) and Support Vector Machine (SVM). The dataset consists of 3,798 reviews, preprocessed from 5,000 initial entries collected from Google Play Store Indonesia, with 2,385 positive and 1,413 negative reviews labeled based on star ratings. Data were split using stratified five-fold cross-validation to ensure balanced class proportions in each fold. Features were extracted with TF-IDF using three N-Gram configurations: unigram, bigram, and unigram+bigram. Results indicate that N-Gram configuration significantly affects the performance of both models. NB achieved the highest accuracy with unigram (87.65%), while SVM performed best with unigram+bigram (88.61% accuracy and 88.22% F1-score). Bigram consistently yielded the lowest performance due to short and informal reviews producing sparser features. This study concludes that N-Gram selection should align with algorithm characteristics, and SVM with unigram+bigram is the most effective approach for sentiment classification of Neobank reviews in Indonesia.
Komparasi FastText dan TF-IDF Berbasis Random Forest pada Analisis Sentimen IKN di Youtube Fadhil Irsyad Ramadhani; Taghfirul Azhima Yoga; Naufal Azmi Verdhika
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.v6i12.9749

Abstract

The development of Indonesia's New Capital City (IKN) represents a significant national policy that has triggered diverse public responses, particularly across social media platforms like YouTube. This study aims to analyze public sentiment regarding the IKN project and compare the performance of two text feature extraction methods, FastText and Term Frequency-Inverse Document Frequency (TF-IDF), using the Random Forest algorithm. The primary objective is to identify which method is more effective in capturing the nuances of Indonesian-language public opinion. The dataset for this research includes 4,093 YouTube comments related to IKN, obtained using the YouTube Data API v3 in August 2025. The data were categorized into two classes, positive and negative, while neutral data were removed to minimize model bias. Data labeling was conducted manually and validated by a linguistic expert, followed by pre-processing stages such as data cleaning, case folding, normalization, tokenizing, stopword removal, and stemming. The setting of a 200-vector dimension for FastText and a 5,000-feature limit for TF-IDF was based on findings from previous sentiment analysis research, proving that such configurations provide stable classification performance compared to other parameters, as they are statistically more effective in filtering irrelevant features without losing deep semantic information. Model performance was evaluated using the 10-Fold Cross-Validation method and Confusion Matrix based on accuracy, precision, recall, and F1-score metrics. Results indicate that the FastText method achieved an accuracy of 83.67%, precision of 84.01%, recall of 83.72%, and an F1-score of 80.83%, while TF-IDF yielded an accuracy of 80.53%. These findings conclude that FastText is more effective in representing the context and semantic meaning of Indonesian YouTube comments related to IKN. Furthermore, this method provides a balance in pattern recognition and the precision of sentiment classification results. This research contributes to assisting stakeholders and researchers in more accurately understanding public opinion toward IKN.
Analisis Dinamik Model Logistik Diskrit Nonlinier Berbasis Simulasi Komputasi dan Bifurkasi Haves Qausar; Zata Hasyyati
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.v6i12.9814

Abstract

The discrete logistic model is a simple nonlinear model capable of producing stable, periodic, and chaotic dynamics; however, chaos identification is often based only on orbit visualisation or bifurcation diagrams. This study aims to analyse its dynamics using analytical results and computational indicators. This study applies mathematical modelling and computational simulation; therefore, it involves neither respondents nor a field research location. The model was analysed using the control parameter , which determines changes in the system dynamics, from to , an initial value of , iterations, and removal of the first iterations as the transient phase. The techniques comprised local stability analysis, time-series simulation, bifurcation diagrams, Lyapunov exponents, and sensitivity to initial conditions. Results show that the zero fixed point is stable for , the nonzero fixed point is stable for , and the first bifurcation occurs at . Period-2, period-4, and period-8 orbits emerge at , , and , respectively. At , the Lyapunov exponent is , and two orbits with an initial difference of diverge, supporting chaotic dynamics. Conversely, at , a period-3 orbit emerges with a Lyapunov exponent of as a periodic window. This study provides a systematic and reproducible computational evaluation framework for nonlinear discrete dynamics.
Pengembangan Sistem Self-Order Kafe Berbasis Web dengan Fitur Promo Bundling Menggunakan K-Means Clustering Salsa Nurul Laeli; Ruci Meiyanti
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.v6i12.9815

Abstract

Digital transformation in the culinary sector faces various operational challenges at Titik Teh Cafe, Kuningan Regency, including long queues at the cashier, order inaccuracies caused by manual communication using handy talkies, and the lack of sales data analysis for 256 menu variants serving an average of 92 customers per day. These problems reduce service efficiency and hinder data-driven decision-making processes. This study aims to develop a web-based self-ordering system using the K-Means Clustering algorithm for the promo bundling feature through an iterative prototyping approach. The system was developed using the Laravel framework, MySQL database, and UML modeling to support system design and implementation. The K-Means method was implemented using sales frequency parameters to form two clusters, namely popular and non-popular menu clusters. The resulting system provides self-ordering and digital payment features while reducing dependence on waiters and minimizing manual communication between cashiers and the kitchen. In addition, the clustering-based promotional feature provides menu popularity analysis to support more effective business strategies and targeted promotional decisions. This system improves the operational efficiency of Titik Teh Cafe and serves as a model for digital transformation in culinary businesses in Indonesia through the integration of self-ordering services and sales analysis.
Implementasi Algoritma Random Forest untuk Prediksi Waktu Penyelesaian Hafalan Al-Qur’an Berbasis Website Muchtar Ali Anwar; Sholihin Sholihin; Muhammad Nur Fajriansyah; Wisnu Chairin
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.v6i12.9832

Abstract

Manual monitoring of Quranic memorization (tahfizh) in Islamic boarding schools faces efficiency challenges due to large student populations and paper-based record keeping. This study aims to implement the Random Forest algorithm to predict the estimated completion time of Quranic memorization in a web-based monitoring system at Madrasah Aliyah Jam’iyyah Islamiyyah, Tangerang Selatan, Indonesia. The dataset consists of 12,458 memorization logs from 271 students during March 1 to May 3, 2026. Feature engineering produced 15 features covering Quranic text complexity, student memorization history, and temporal patterns; Spearman correlation feature selection reduced these to 13 significant features. The model was optimized using GridSearchCV and evaluated with MAE, RMSE, R², MAPE, and 5-fold cross-validation. Random Forest achieves R²=0.8966, MAE=0.6141, and MAPE=6.98% on the 70:30 split, outperforming Decision Tree (R²=0.8879) and matching XGBoost (R²=0.8964). Cross-validation yields CV R²=0.9004, confirming stable generalization. Feature importance analysis indicates that student learning habits are stronger predictors than Quranic text complexity. As a practical contribution, the model is integrated into a web-based monitoring system enabling teachers to track all students’ progress centrally and receive automated memorization completion estimates, enhancing the effectiveness of guidance in tahfizh institutions.

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