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Contact Name
Sarida Sirait
Contact Email
saridasrt@gmail.com
Phone
+6281319494217
Journal Mail Official
saridasrt@gmail.com
Editorial Address
Jl. Sriwijya No. 9 C-E Pematangsiantar, Sumatera Utara
Location
Kota pematangsiantar,
Sumatera utara
INDONESIA
Jurnal Tekinkom (Teknik Informasi dan Komputer)
ISSN : 26211556     EISSN : 26213079     DOI : https://doi.org/10.37600/tekinkom
Core Subject : Science,
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.
Articles 407 Documents
PREDIKSI WAKTU KELULUSAN MAHASISWA BERDASARKAN FAKTOR AKADEMIK DAN DEMOGRAFIS MENGGUNAKAN RANDOM FOREST DAN XGBOOST Supahri, Hafid Azis; Erlinda, Susi; Nasution, Torkis; Asnal, Hadi
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.v8i1.2308

Abstract

Accurate graduation time is an important measure to illuminate how well the higher education system functions. Data from 10,000 students was used, including GPA, credits, age, gender, place of residence, employment status, economic status, and scholarship acceptance. Class imbalance in the data is addressed through the CRISP-DM and SMOTE methods. The evaluation results show that both algorithms have the capability to predict permit status with high accuracy; Random Forest achieved an accuracy of 91.95% and XGBoost 91.85%. Based on the precision, recall, and F1 score, both models demonstrate very good and balanced performance, with Random Forest being slightly superior in result stability. Therefore, Random Forest is recommended as the best model for graduation prediction. This research is expected to help colleges identify students who may graduate late to provide timely interventions.
PENGEMBANGAN SISTEM INFORMASI PENGARSIPAN KINERJA DOSEN ABK PRIMANIYARTA DENGAN METODE SDLC Setlight, Durand Fernandito Freddy; Supriyanto, Supriyanto; Canon, Lidya Juwita Pratiwi
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.v8i1.2330

Abstract

The management and archiving of lecturer performance data are crucial for accountability, professional development, and meeting higher education accreditation standards. In many universities, like ABK Primaniyarta, the collection and archiving of performance evidence (e.g., teaching, research, and community service reports) are often done manually. One of the key needs for ABK Primaniyarta is an archiving information system or e-repository. Therefore, this research focuses on the design and development of a lecturer performance archiving system based on the Software Development Life Cycle (SDLC) using the Waterfall model, which includes requirements analysis, design, implementation, testing, and maintenance. In this application, document input will be performed by lecturers acting as administrators/users. The main objective of this application is to facilitate lecturers in submitting their performance data to each other and to ensure it can be properly archived. Data collection techniques involved interviews with lecturers and observations of the existing document reporting and archiving system at ABK Primaniyarta. The outcome of this research is an official web-based information system for archiving and reporting lecturer performance at ABK Primaniyarta, eliminating the need for Email & Google Drive.
ANALISIS PERBANDINGAN THROUGHPUT PADA SINGLE SERVER DAN CLUSTER SERVER MENGGUNAKAN LOAD BALANCING Farida, Farida; Azhari, Lukman; Kaaffah, Faiz Muqorrir; Wartono, Wartono
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.v8i1.1660

Abstract

The rapid growth of internet usage has significantly increased the demand for web servers, necessitating efficient resource management solutions. To address this challenge, load balancing has become a crucial strategy for distributing incoming connection requests across multiple servers hosting the same content. This approach enhances performance and service reliability by optimizing workload distribution. This study aims to develop and evaluate a load balancing model using clustered servers to handle high workloads efficiently. The test results indicate that the clustered server system with load balancing consistently achieves higher throughput compared to a single-server-based system. In a single-server configuration, throughput ranges from 2,640 KB/s to 2,677 KB/s, while in a clustered server setting with load balancing, throughput improves to 2,704 KB/s to 2,731 KB/s. These findings underscore the importance of load balancing in ensuring optimal and efficient performance on web servers, both in local and distributed scenarios.
ANALISIS PENGEMBANGAN WEBSITE INTERAKTIF DAN USABILITY LAYANAN INFORMASI PENDAFTARAN PROGRAM BANTUAN PADA DINAS SOSIAL KOTA MEDAN Devraz, R.W.; Sitanggang, Jelita Sari; Pardede, Eva Damayanti; Sihombing, Oloan
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.v8i1.1972

Abstract

This study suggests the development of an interactive website to improve user-friendly services and information and make registration for social assistance programs easier at the Dinas Sosial of Medan City. The main objective of the research is to address the problems that people often face: ineffective online registration process and lack of information about social assistance programs. The research method was conducted by creating a website with an easy-to-use user interface and testing it with user research methods to ensure that it was easily accessible and useful. The results show that a clear, complete, and easy-to-use website can increase transparency and public engagement in social assistance programs. Therefore, it is hoped that the creation of this interactive website will be an effective tool to improve the availability of information and services and make it easier for the public to get the assistance they need.
PENGEMBANGAN APLIKASI SURAT MENYURAT DARI KECAMATAN MEKAR BARU KE DESA BERBASIS MOBILE Noviyanti, Ayu; 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.v8i1.2367

Abstract

The objective of this research is to address issues with correspondence administration in Mekar Baru Subdistrict, which still uses physical documents, causing problems such as lost or damaged letters and difficulties in monitoring letter disposition in real time. The SiMekar mobile application was created using the Flutter framework and the Waterfall software development method as a solution. The main features of this application include incoming and outgoing letter management, digital disposition, real-time notifications, and role-based user authentication and authorisation. Interviews, observations, and documentation of administrative processes were used to collect data. The results of observations and testing show that all application functions operate in accordance with user needs, and the digitalisation of letter-writing processes using this application can enhance the speed, accuracy, efficiency, and transparency of letter management in sub-district governments. Therefore, the SiMekar application is a useful digital solution to support public administration transformation through the use of information technology.
ANALISIS SENTIMEN KOMENTAR YOUTUBE TENTANG DEMAM BERDARAH DENGUE MENGGUNAKAN NAIVE BAYES Rasyidin, Andi; Febriandirza, Arafat
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.v8i1.2239

Abstract

This study aims to analyze public sentiment towards Dengue Hemorrhagic Fever (DHF), a disease that is still a serious health problem in tropical countries such as Indonesia. This problem is explored through sentiment analysis of 1.058 user comments taken from four YouTube videos related to DHF, symptoms, treatment, and recovery. Text preprocessing is applied to the comments, followed by sentiment labeling using InSet Lexicon, and classification using the Multinomial Naive Bayes algorithm. To address class imbalance, the SMOTE (Synthetic Minority Oversampling Technique) method is applied. The dataset is divided into three ratios (70:30, 80:20, and 90:10) to evaluate model performance using Balanced Accuracy, AUC Score, and G-Mean. The result show that the application of SMOTE significantly improves the model’s ability to classify the minority class. The best performance was achieved with a train-test ratio of 70:30, resulting in a Balanced Accuracy of 0.7818, an AUC Score of 0.9357, and a G-Mean of 0.8396. These findings indicate that the combination of Naive Bayes and SMOTE is effective for sentiment classification of imbalanced social media data and can support public health communication strategies
ANALISIS SENTIMEN MASYARAKAT INDONESIA TERHADAP PELUNCURAN DANANTARA MENGGUNAKAN METODE SUPPORT VECTOR MACHINE Sari, Retno; Agustin, Agustin; Rahmiati, Rahmiati; Junadhi, Junadhi
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.v8i1.2351

Abstract

As one of the latest innovations in Indonesia, the launch of Danantara has attracted public attention, especially the X (formerly Twitter) social media community. Understanding public sentiment about the launch is crucial due to the varied reactions. Using the Support Vector Machine (SVM) method as the primary classification algorithm, this study aims to examine how Indonesians perceive the launch of Danantara. Data collected through scraping techniques from social media posts were then processed through text preprocessing processes such as data cleaning, tokenization, and normalization. Categorizing sentiment into positive, negative, or neutral can be done using the signal variable model (SVM). The results show that the majority of the public has a certain sentiment towards the launch of Danantara, as the SVM model can classify sentiment very accurately. In the future, this study will help stakeholders understand public opinion and create better communication plans.
ANALISIS PERBANDINGAN ALGORITMA C4.5 DAN NAIVE BAYES UNTUK MEMPREDIKSI KETERCAPAIAN TARGET PO DALAM MEMBANGUN PROJECT FTTH (FIBER TO THE HOME) Pratama, Ahmad Tara; Deni, Rahmad; Agustin, Agustin; Asnal, Hadi
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.v8i1.2309

Abstract

In the digital era, the demand for high-speed and stable internet has become essential to support communication and information access. Fiber to the Home (FTTH) is one of the main solutions implemented by internet service providers such as MyRepublic. A critical component in FTTH network development is the issuance of Purchase Orders (PO) to vendors, which directly impacts the achievement of sales targets. This study aims to compare the performance of the C4.5 and Naïve Bayes classification algorithms in predicting PO target achievement to assist project planning and decision-making. The research uses historical data from FTTH projects and applies data partitioning scenarios of 70:30, 80:20, and 90:10 for model training and testing. Evaluation was conducted using accuracy as the main performance metric. The results show that the Naïve Bayes algorithm achieved the highest accuracy of 85.64% with a 70:30 data split, while C4.5 obtained 83.54% accuracy with a 90:10 data split. Based on these findings, the Naïve Bayes algorithm is considered more effective and consistent in predicting PO target achievement and is recommended for implementation in similar project scenarios.
RANCANG BANGUN APLIKASI GAME EDUKASI MENYUSUN HURUF (SCRAMBLE LETTER) BAHASA INGGRIS BERBASIS ANDROID UNTUK MENINGKATKAN KOSAKATA TINGKAT SD Ahkam, Mizanul; Melisa, Anis; Kurniawati, Kurniawati; Umran, Munzir; Elfisa, Elfisa; Jakfar, Abu Bakar; Sara, Fahmi
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.v8i1.2394

Abstract

The limited mastery of English vocabulary among elementary school students often becomes a barrier to their language learning progress, highlighting the need for engaging and interactive learning media. This study aims to develop an Android-based educational game application named Scramble Letter as an alternative tool to support vocabulary acquisition. The application was developed using Unity with an attractive interface and progressive gameplay consisting of Easy, Medium, and Hard levels. The research employed an experimental approach through observation, functional testing on various Android devices, and questionnaires administered to 21 elementary school students. The results revealed that over 90% of respondents agreed the application increased their learning motivation, enriched vocabulary, and improved spelling comprehension. In addition, functional testing demonstrated that the game operated stably without significant technical issues. These findings indicate that the Scramble Letter game is feasible and effective as a technology-based learning medium to enhance English vocabulary mastery among young learners.
DIAGNOSIS CEREBROVASCULAR ACCIDENTS MENGGUNAKAN TEKNIK SMOTEEN DENGAN MEMBANDINGKAN METODE KLASIFIKASI DECISION TREE DAN XGBOOST Fadli, Muhammad; Purwanti, Dian Sri; Surono, Muhammad; Dewantoro, Mahendra; Suryono, Ryan Randy
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.v8i1.2025

Abstract

Cerebrovascular Accident (stroke) is a critical health issue in Indonesia, often leading to high mortality and long-term disability. Early detection through machine learning has emerged as a promising approach to improve diagnosis and treatment outcomes. This study aims to compare the performance of two classification algorithms, Decision Tree and Extreme Gradient Boosting (XGBoost), in diagnosing stroke using the SMOTEENN (Synthetic Minority Over-sampling Technique and Edited Nearest Neighbor) technique to address data imbalance. The dataset used contains 5110 samples with 11 independent variables and one dependent variable (stroke status), obtained from a public repository. After preprocessing and data balancing, both models were trained and evaluated based on accuracy, precision, recall, and F1-score. The results show that XGBoost outperforms Decision Tree in all evaluation metrics, achieving an accuracy of 96.48%, precision of 94.75%, recall of 99.03%, and F1-score of 96.85%, compared to Decision Tree’s accuracy of 91.55%, precision of 89.82%, recall of 95.32%, and F1-score of 92.49%. These findings confirm that the combination of XGBoost and SMOTEENN provides a more effective and reliable classification model for early stroke diagnosis. Future research is encouraged to explore deep learning techniques to further enhance diagnostic accuracy.