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TIN: TERAPAN INFORMATIKA NUSANTARA
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27227987     DOI : -
Core Subject : Economy, Health, Science, Education, Art, Social, Engineering,
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other
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 582 Documents
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Klasifikasi Rentang Gaji Lowongan Pekerjaan di Glints Wilayah Jabodetabek Menggunakan Regresi Logistik dan Random Forest Berbasis Web Scraping Banjarnahor, Evander; Setiawan, Theodore Miracle; Charlest, Wellson Antonio; Belferik, Ronald
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.9193

Abstract

Digital transformation has reshaped the labor market, with online platforms such as Glints serving as large-scale data repositories that connect job seekers with employers. In the Greater Jakarta (Jabodetabek) region, salary information is a critical factor in career decision-making; however, salary-related information asymmetry remains a major challenge. This study begins with a descriptive analysis of 1,497 job vacancies collected through web scraping techniques to examine salary distributions across locations and employment statuses. The salaries were classified into three categories: low salary (≤ IDR 5 million), medium salary (IDR 5–10 million), and high salary (≥ IDR 10 million). The results indicate that the majority of job vacancies fall into the low-salary category (77.09%), followed by the medium-salary category (21.37%), while high-salary positions constitute only 1.54% of the total dataset. Subsequently, this study aims to develop salary category classification models by comparing two machine learning methods: Logistic Regression and Random Forest. Model performance was evaluated using accuracy, precision, recall, and F1-score under multiple training–testing split scenarios. The experimental results demonstrate that Random Forest consistently outperforms Logistic Regression, achieving a highest accuracy of 98.00%, compared to approximately 79% for Logistic Regression. These findings suggest that the relationship between job characteristics and salary categories is complex and non-linear, making it more effectively captured by ensemble-based, non-linear algorithms such as Random Forest. This study contributes to improving salary transparency and supports the development of more accurate and data-driven salary prediction systems.
Peningkatan Kinerja Algoritma FP-Growth Untuk Analisis Pola Pembelian Pelanggan Menggunakan Algoritma Optimasi Tabu Search Nurrahman, Sintya Fadillah; Via, Yisti Vita; Al Haromainy, Muhammad Muharrom
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.9227

Abstract

Along with the rapid development of technology, the volume and scale of data stored by business entities continue to increase, particularly sales transaction data that contain valuable information to support decision-making and business development. Therefore, this study aims to analyze customer purchasing patterns by combining the Tabu Search and FP-Growth algorithms. Tabu Search is applied as a preprocessing stage to filter and sort transaction data before further analysis using the FP-Growth algorithm as an association analysis method. The results of applying these algorithms are association rules that represent relationships among items and can be used as a basis for business decision-making. The evaluation is conducted using support, confidence, and lift metrics to assess the strength of the generated rules, as well as execution time and the number of itemsets to compare the performance of FP-Growth with and without Tabu Search. The experimental results show that Tabu Search is able to effectively filter itemsets, where at a minimum support value of 0.01 the number of itemsets is reduced from 1,390 to 237, and at a minimum support value of 0.1 from 64 to 34. Although the combination of Tabu Search and FP-Growth requires a longer execution time due to the iterative process of Tabu Search, the resulting patterns are more focused, demonstrating the effectiveness of Tabu Search in improving the efficiency and quality of customer purchasing pattern analysis.
Pengembangan Sistem Manajemen Stok Bahan Baku Roti Berbasis Web Menerapkan Metode Agile Sudiro, Fuji; Septanto, Henri
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.9234

Abstract

PT. Nozawa Food Indonesia still faces challenges in raw material management due to stock recording processes that are carried out manually and are not yet digitally integrated. This study aims to develop a web-based inventory information system that implements the First In First Out (FIFO) method to support more organized raw material tracking. The research was conducted within the company environment using primary data obtained through observation and interviews. The system was developed using the Agile approach, allowing for iterative development from planning to evaluation. The system includes features such as stock-in and stock-out recording, minimum stock notifications, and stock reports accessible to relevant departments. The resulting system is expected to provide an efficient solution for stock documentation and support faster, data-based decision-making processes.
Analisis Efektivitas Penggunaan Sistem Informasi Dalam Mendukung Aktivitas Pembelajaran Jamariani, Jamariani; Amalia, Alfi
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.9244

Abstract

This study aims to analyze the effectiveness of using a Learning Management System (LMS), specifically Google Classroom, along with the school’s internal Academic Information System, in supporting learning activities at Darul Mahdiyah Private School, Thailand. A qualitative descriptive approach was employed through observations, interviews, and document analysis involving school administrators, teachers, and students. The findings indicate that the use of the LMS at the upper secondary level improves the organization of learning schedules, material distribution, assignment submission, and attendance recording, while also shifting teacher–student interaction toward more flexible and asynchronous communication. In contrast, learning activities at the primary and lower secondary levels remain predominantly manual. The study also reveals a dual-adaptation pattern, in which teachers combine digital and manual systems as a transitional strategy due to varying levels of technological literacy, highlighting that system effectiveness is influenced not only by technological availability but also by human resource readiness and institutional support.
Penerapan Natural Language Processing Dalam Klasifikasi Sentimen Komentar Youtube Tentang Judi Online Lase, Fransiskus Oktanesius; Pieter S, Yoel; Lase, Kristian Juri Damai
TIN: Terapan Informatika Nusantara Vol 6 No 9 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i9.9256

Abstract

YouTube, as a video-sharing platform, has become a public interaction space rich in opinions regarding online gambling issues in Indonesia. However, large-scale manual sentiment analysis is difficult due to the high data volume and local language nuances. This study aims to develop a sentiment classification model for Indonesian-language YouTube comments using Natural Language Processing (NLP) techniques to understand public perceptions of the online gambling phenomenon. Data of 3,000 comments were collected from YouTube videos related to online gambling through the YouTube Data API in Indonesia. All data were manually annotated by three annotators (kappa 0.85) into three sentiment classes (positive, negative, neutral) along with relevance, then divided into 80% training and 20% testing. Pre-processing included case folding, text cleaning, tokenization, stopword removal, stemming, lemmatization, and slang normalization. Models tested included Naive Bayes, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and IndoBERT. Evaluation using accuracy, precision, recall, and F1-score metrics showed IndoBERT achieved the best performance with 91.67% accuracy, 90% precision (negative class), 95% recall (negative class), and 91.66% F1-score. This research contributes to understanding public attitudes toward online gambling and the development of an adaptive sentiment classification system for the Indonesian language.
Analisis Metode Elbow SSE, Silhouette Score, dan Jaccard Stability dalam Pemilihan Jumlah Klaster Data yang Optimal Hartono, Budi; Lusiana, Veronica
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.9271

Abstract

This study discusses the selection of the optimal number of clusters (K) in the K-Means algorithm by utilizing a combination of the Elbow method with the SSE (Sum of Squared Errors) and Silhouette Score metrics. The main problem is that the optimal K value is unknown. Choosing K that is too small can combine different patterns (under-clustering), and choosing K that is too large can break the same pattern into several clusters (over-clustering). The experiment used two-dimensional test data with variations in the number of data 20, 30, 40, 50, and 60. K-Means was run in the range of K = 2 to K = 8, then the SSE value was calculated to form the Elbow curve and the average Silhouette value to evaluate the quality of the cluster. This study added a cluster stability test using the Jaccard Stability value. The highest Silhouette value of 0.4619 was obtained from the data 20 for K = 2. The highest Jaccard stability value of 0.9507 was obtained from 60 data sets for K = 2. The experimental results show that the Elbow method, Silhouette value, and Jaccard stability can be used complementarily in determining the optimal K. In some test data, both metrics produce consistent K recommendations, while in certain test data, Elbow can provide several candidates, so that validation using the Silhouette value is needed to select the optimal K.
Youth-Oriented Digital Media Visibility on Instagram: A Multimodal Qualitative Analysis of USSFeeds Ridho, Muhammad Rajwa; Sumarlan, Iman
TIN: Terapan Informatika Nusantara Vol 6 No 9 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i9.9272

Abstract

Social media platforms have reshaped how visibility is produced, sustained, and contested within contemporary digital media environments. Despite extensive research on social media branding and engagement, limited attention has been given to how youth-oriented digital media actors strategically construct visibility as an ongoing communicative process rather than mere exposure. This study aims to examine how a youth-oriented digital media account builds and maintains visibility on Instagram through everyday visual, narrative, and participatory practices. Employing a qualitative content analysis, this research analyzes selected Instagram posts published by USSFeeds between March and September 2025. The analysis focuses on three analytical dimensions: visual discourse, narrative framing, and participatory circulation. The findings indicate that visual coherence operates as a semiotic anchor that stabilizes media identity, while narrative framing grounded in youth culture enhances cultural resonance and interpretive alignment. The audience participation amplifies visibility by enabling relational circulation of meaning within platform-mediated and algorithmically structured environments. This study contributes to media and communication scholarship by conceptualizing digital visibility as a relational and processual phenomenon shaped by discourse, participation, and platform logics, thereby extending existing understandings beyond linear models of attention and reach.
Evaluasi Gangguan Jaringan Distribusi Tegangan Menengah Menggunakan Failure Mode and Effect Analysis (FMEA) Mubarok, Kholifah Yusuf; Marbun, Musa Partahi
TIN: Terapan Informatika Nusantara Vol 6 No 9 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i9.9280

Abstract

Disturbances in the medium voltage distribution network at PT PLN Persero UP3 Makassar Selatan cause repeated outages that disrupt supply continuity in a major economic hub of Eastern Indonesia, with 1,207 incidents recorded in 2024 dominated by the rainy season. This study aims to quantify unserved energy losses for each disturbance type, determine the severity and occurrence levels of failure modes, apply Failure Mode and Effect Analysis (FMEA) for risk mapping, and formulate preventive mitigation strategies. Historical outage data from 2024 and expert judgements from distribution maintenance personnel are processed through three‑phase power and Energy Not Supplied (ENS) calculations, severity–occurrence scaling, FMEA tabulation, and a 5×5 risk matrix to classify risk levels. The results show that lightning/extreme weather, trees/vegetation, animals, and “unidentified” causes generate the largest ENS and fall into the highest risk classes, while internal equipment failures generally remain at moderate to low risk. Recommended actions include reinforcing lightning protection and grounding, systematic vegetation management, installation of animal protection devices, improvement of joints and construction quality, optimization of protection operation, and community/social stakeholder outreach, which are estimated to be more cost‑effective than tolerating recurring outage losses.
Analisis Kualitas Sistem E-Rapor SP Menggunakan Metode WebQual 4.0 dan IPA Ekayanti, Gusti Ayu Mas; Saskara, Gede Arna Jude; Putra, I Nyoman Tri Anindia
TIN: Terapan Informatika Nusantara Vol 6 No 9 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i9.9318

Abstract

One form of information technology implementation in education is the use of the e-Rapor SP system in preparing students’ learning outcome reports. However, users still experience various difficulties in its implementation, indicating that the system quality needs improvement. This study aims to measure the quality of the e-Rapor SP system at the junior high school level based on users’ perceptions in Buleleng Regency and to identify low-performing aspects for improvement recommendations. System quality was measured using the WebQual 4.0 variables, including usability, information quality, service interaction quality, and overall impression. This study employed a quantitative approach using a survey method. Data were collected from 84 respondents through a WebQual 4.0-based questionnaire and analyzed using descriptive statistics, the WebQual Index, and Importance Performance Analysis (IPA). The results show that the e-Rapor system is perceived as having good quality but has not yet met users’ expectations. The WebQual Index value of 0.78 indicates a good but not ideal level of system quality. Gap and conformity analyses revealed negative gaps and conformity values below 100%, indicating a mismatch between system performance and user expectations. The recommended improvements focus on enhancing service quality through clearer helpdesk support or guidance, as well as faster and more consistent service responses. The findings of this study provide a basis for decision-making to improve the quality of the e-Rapor system.
Penerapan Naive Bayes dalam Mengidentifikasi Penyebab Remaja Desa Terlibat dalam Judi Online Annabil, M Haziq; R, Rakhmat Kurniawan
TIN: Terapan Informatika Nusantara Vol 6 No 9 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i9.9350

Abstract

The development of digital technology and increased internet access have driven changes in adolescent social behavior, including increased involvement in online gambling activities, not only in urban areas but also in rural areas. This study aims to identify the factors that influence adolescent involvement in online gambling and to measure the level of influence of each factor using a machine learning-based classification approach. The research was conducted in Payageli Village, Sunggal District, Deli Serdang Regency, involving 507 adolescent respondents who were surveyed using a questionnaire containing 23 questions. The data obtained was analyzed using the Naive Bayes algorithm with data preprocessing, categorical attribute coding, and data division into training and test data with a ratio of 80:20. The results showed that the dominant factors influencing adolescent involvement in online gambling included peer influence, intensity of exposure to online gambling advertisements, online gaming habits, low positive leisure activities, and lack of parental supervision. The classification model built produced an accuracy rate of 98%, with high precision and recall values in each class. These findings indicate that the Naive Bayes algorithm is effective in identifying adolescents at risk of engaging in online gambling and has the potential to be used as a basis for developing data-driven prevention strategies at the village level.

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