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Jurnal Tekinkom (Teknik Informasi dan Komputer)
Published by Politeknik Bisnis Indonesia
ISSN : 26211556     EISSN : 26213079     DOI : https://doi.org/10.37600/tekinkom
Core Subject : Science,
Computer Science & IT
Jurnal TEKINKOM merupakan jurnal yang dimaksudkan sebagai media terbitan kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai isu Ilmu - ilmu komputer dan sistem informasi, seperti : Pemrograman Jaringan, Jaringan Komputer, Teknik Komputer, Ilmu Komputer/Informatika, Sistem Informasi, dan Multi Disiplin Penunjang Domain Penelitian Komputasi, Sistem dan Teknologi Informasi dan Komunikasi, dan lain-lain yang terkait. Artikel ilmiah dimaksud berupa kajian teori (theoritical review) dan kajian empiris dari ilmu terkait, yang dapat dipertanggungjawabkan serta disebarluaskan secara nasional maupun internasional.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 407 Documents
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PENGEMBANGAN SISTEM PRESENSI MENGGUNAKAN QR CODE BERBASIS WEBSITE PADA SISWA SMK Aryo Bagaskoro, Yoga; Wantoro, Jan
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2091

Abstract

The attendance system in many vocational high schools (SMK) is still conducted manually, which often leads to inefficiencies, data manipulation, and loss of records. This research aims to develop a web-based student attendance system using QR Code technology to address those issues. The system was developed using the Waterfall model, comprising five stages: requirements analysis, system design, implementation, verification, and maintenance. The system was built using PHP with the CodeIgniter framework and MySQL database, and was designed to be user-friendly for both teachers and students. The results showed that the QR Code-based attendance system successfully automated the recording of student attendance, minimized errors, and generated real-time attendance reports. Blackbox Testing was used to evaluate the system’s functionality, yielding a 100% success rate. Maintenance was carried out to fix minor bugs and interface inconsistencies without altering the core features. Overall, the system enhances the efficiency and accuracy of attendance management in schools and supports the digital transformation of educational administration.
ANALISIS SENTIMEN TERHADAP MASJID RAYA AN-NUR PROVINSI RIAU MENGGUNAKAN TEKNIK STACKING MACHINE LEARNING Malya, Vivi Triani; Erlinda, Susi; Hamdani, Hamdani; Yanti, Rini
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2305

Abstract

This study aims to analyze public sentiment regarding Masjid Raya An-Nur in Riau Province using a machine learning stacking technique. Visitor reviews collected from Google Maps are classified into three categories: Facilities, Cleanliness, and Security. The research applies several preprocessing stages including cleaning, normalization, and tokenization, followed by TF-IDF weighting. To address class imbalance, SMOTE is used before the training process. Three base models—K-Nearest Neighbors (KNN), Decision Tree (DT), and Multinomial Naïve Bayes (MNB)—are trained, and their outputs are combined using Logistic Regression as the meta-classifier in a stacking ensemble. The results show that the stacking model outperforms the individual models with an accuracy of 94%, compared to 73% for KNN, 92.8% for DT, and 83.8% for MNB. The stacking technique provides high and balanced precision, recall, and F1-scores across all sentiment categories. This approach demonstrates the effectiveness of ensemble learning in improving sentiment classification performance for unstructured textual data. The findings are expected to help mosque administrators gain deeper insights into public perceptions and enhance service quality.
EVALUASI USABILITY TERHADAP SIAM UNPRI MENGGUNAKAN METODE HEURISTIC EVALUATION DAN USER TESTING br Bangun, Agita Putri; Octaviani, Vina; Pasaribu, Gres Audia; Harmaja, Okta Jaya
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.1976

Abstract

This study aims to assess the level of usability of the Student Academic Information System (SIAM) at Universitas Prima Indonesia (UNPRI) with a heuristic evaluation and user testing approach. Heuristic evaluation was conducted by three expert evaluators in the field of UI/UX and 21 problems were found during the trial process. Based on the results of heuristic evaluation testing, redesign and user testing of the redesign were carried out. Quantitative data obtained using the System Usability Scale (SUS) questionnaire, obtained a score of 80.5, which indicates that the redesigned system is in the “acceptable” category, but some improvements need to be made. Design improvement recommendations from these findings have been developed to improve the quality of system usage, especially SIAM UNPRI.
SIMULASI PENGENDALI SUHU DAN KELEMBABAN PADA BUDIDAYA JAMUR TIRAM DENGAN METODE FUZZY LOGIC SUGENO: PENDEKATAN KONSEPTUAL Blegur, Ernes Josias; Bonnu, Christin Hendriyani
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2068

Abstract

Oyster mushroom (Pleurotus ostreatus) cultivation requires ideal environmental conditions, particularly temperature and humidity parameters, to achieve optimal growth. This study aims to evaluate the effectiveness of applying fuzzy logic through the Sugeno method in developing an automatic control system for oyster mushroom cultivation, with the study limited to the system simulation stage. The simulation results show that the Sugeno method is capable of producing high accuracy in the decision-making process and adaptive adjustment of environmental parameters. The developed system is able to maintain the average value of air humidity in the range of 80–90% and air temperature between 22–28°C, in accordance with the ideal conditions for oyster mushroom growth. This research can be a foundation for further development in the implementation of efficient and sustainable control systems, especially in supporting oyster mushroom farmers in the North Central Timor region.
PENERAPAN GRADIENT BOOSTING MACHINES UNTUK MEMPREDIKSI PROMOSI JABATAN KARYAWAN Kurniawan, Fadly; Tashid, Tashid; Unang Rio, Unang Rio; Agustin, Wirta
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2307

Abstract

Job promotion is an important factor in human resource management as it can enhance employee motivation, loyalty, and performance. This study aims to build a job promotion prediction model using the Gradient Boosting Machines (GBM) algorithm implemented in RapidMiner Studio. The dataset used was sourced from Kaggle, consisting of 54,808 training records and 23,491 testing records. The research process included data preprocessing, splitting into training and testing sets, model training, performance evaluation using metrics such as accuracy, precision, recall, F1-score, and AUC, and applying the model to actual test data. The developed GBM model achieved an accuracy of 91.10% and an AUC value of 0.776. The prediction results on the test data indicated that approximately 84.4% of employees were predicted as not eligible for promotion, while 15.6% were predicted as eligible. These findings demonstrate that a machine learning approach can help companies make job promotion decisions more objectively, transparently, and data-driven.
ANALISIS SENTIMEN ULASAN APLIKASI MEDIA SOSIAL WHATSAPP DAN TELEGRAM BERDASARKAN ULASAN PLAY STORE MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE Ginting, Yudhi Aginta Pranata; Ndraha, Esterina; Fahmi, Mohammad Irfan
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.1979

Abstract

The rapid development of mobile-based information technology has increased the relevance of sentiment analysis on user-generated reviews. This study applies the Support Vector Machine (SVM) method combined with TF-IDF feature extraction to classify user sentiments from WhatsApp and Telegram reviews on the Play Store. A total of 4,400 Indonesian-language reviews (2,200 per application), collected via web scraping from different time periods in 2024, were processed through standard text preprocessing techniques and transformed using TF-IDF with 1–2 n-grams. The SVM model with a linear kernel was trained on 80% of the data and tested on 20% using accuracy, precision, recall, and F1-score metrics. Results show that Telegram reviews achieved higher accuracy (83%) and F1-score (0.83) compared to WhatsApp (68% accuracy, 0.73 F1-score). Sentiment analysis revealed a positive sentiment dominance in WhatsApp (~60%) and negative sentiment in Telegram (~52%). These findings suggest that Telegram reviews tend to have more concise and structured language, contributing to better classification performance. The study confirms the effectiveness of the SVM–TF-IDF approach and recommends further research using advanced models and embeddings to handle the complexity of informal review language.
IMPLEMENTASI ALGORITMA K-MEANS UNTUK CLUSTERING DATA PENYANDANG MASALAH KESEJAHTERAAN SOSIAL DI WILAYAH SUMATERA UTARA Tambunan, Daniel Partogi; Willy, Willy; Fertomedis, Ridho Lukas; Mujahid, Putra Edi
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.1802

Abstract

Penyandang Masalah Kesejahteraan Sosial (PMKS)are community groups facing difficulties in meeting basic life needs due to factors such as poverty, disability, drug dependence, and disasters. In North Sumatra Province, the distribution of PMKS varies significantly across regions, requiring targeted and region-specific social policies. This study aims to cluster 33 districts/cities based on the number and types of PMKS in 2022 using the K-Means algorithm. This method was selected for its effectiveness in uncovering patterns from complex datasets. The clustering process involved multiple iterations until convergence, and the results were validated using RapidMiner. The findings reveal three clusters: Cluster 1 (two regions) with a high level of PMKS, Cluster 2 (six regions) with a moderate level, and Cluster 3 (25 regions) with a low level. Regions with high PMKS levels, such as Batu Bara and Karo, show dominance in categories like extreme poverty, disability, and socially vulnerable women. The results provide a clearer picture of social welfare conditions across the region and serve as a valuable reference for designing more focused and efficient social welfare policies tailored to regional needs.
ANALISIS SENTIMEN PENGGUNA LAYANAN TRANSPORTASI ONLINE GOJEK DAN INDRIVER DENGAN ALGORITMA SUPPORT VECTOR MACHINE Siburian, Yosepha; Cristian, Yusticio; Puri, Anin Dita Sekar; Manurung, Onesimus Gust; Tamba, Saut Parsaoran
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.1973

Abstract

Online transportation is a service that utilizes digital technology, such as mobile applications, to make it easier for users to book trips with just a touch on the phone screen. This convenience makes online transportation increasingly popular, especially in big cities in Indonesia. Two online transportation services that are widely used in Indonesia are Gojek and InDrive. These two platforms are inseparable from user opinions, which greatly affect the desires and competitiveness of each service. This study aims to conduct sentiment analysis on user reviews from the Google Play Store in order to compare the two applications using the Support Vector Machine (SVM) algorithm. This study shows that most of the 1000 Gojek and InDrive user reviews on the Play Store contain negative sentiment, especially related to driver delays and application disruptions. The Support Vector Machine (SVM) algorithm provides the highest accuracy of 86.11% at a training and test data ratio of 80:20, and shows relatively stable performance at other ratios. The dominant words in positive reviews are "good" and "fast", while in negative reviews are "old" and "cancel", indicating that the speed and brightness of the service are the main concerns of users. SVM is effective in analyzing online transportation review sentiment with high accuracy, and the analysis results reveal that users convey more complaints, especially regarding the speed and reliability of Gojek and InDrive services.
RANCANG BANGUN APLIKASI E-LEARNING MOBILE BERBASIS FLUTTER DI MA AL-ITTIHAD Fasya, Nabiila; Aryono, Gagah Dwiki Putra; Auliana, Sigit
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2364

Abstract

The limitations of implementing digital learning methods at MA Al-Ittihad Pedaleman Tanara, particularly in the subject of Informatics, have resulted in low access to learning materials and interactions. This study aims to design and develop a mobile-based e-learning application using the Flutter framework to enhance the effectiveness, flexibility, and accessibility of the learning process. The development method used is the waterfall model, which consists of five stages: needs analysis, design, implementation, testing, and evaluation. The developed application provides key features such as digital learning materials, interactive practice questions, discussion forums, online evaluations, and task reminder notifications. Testing results show that the application runs well, supports interactive and flexible learning activities, and has received positive responses from students and teachers due to its ease of use and suitability to their needs. Thus, this application is expected to become an effective digital learning solution for madrasahs and contribute to promoting digital transformation in Islamic education, especially in areas that have not yet optimally utilized information technology.
RANCANG BANGUN SISTEM INFORMASI PEMESANAN ROTI BERBASIS WEBSITE PADA TOKO ROBADO BAKERY MENGGUNAKAN METODE WATERFALL Sutresna, I Made; Putra, Made Adi Paramartha; Purnama, I Nyoman
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.1904

Abstract

The rapid development of information technology has become an essential element in various aspects of life, including the business sector. Robado Bakery, which has been operating since 2018 in Nusa Penida, faces challenges in managing orders, which are still manually processed through WhatsApp. This practice leads to unorganized record-keeping, overproduction, and the unavailability of required bread stock. To address these issues, this study aims to design and develop a web-based bread ordering information system. This research adopts the waterfall development method, encompassing stages such as requirements analysis, system design, implementation, testing, and maintenance. Data collection was conducted through observation, interviews, and literature studies. The results show that the waterfall method remains relevant for the design and development of structured and integrated web-based information systems, particularly in the context of order management. The implemented system successfully improves order tracking, displays real-time stock information, and supports pre-order functions, thereby optimizing store operations and reducing operational inefficiencies.

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