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Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika
Published by Universitas Palangka Raya
ISSN : 1907896X     EISSN : 26560321     DOI : https://doi.org/10.47111/JTI
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science
Jurnal Teknologi Informasi (JTI) diterbitkan adalah Jurnal Jurusan Teknik Informatika Universitas Palangka Raya dengan ISSN 1907-896X, E-ISSN 2656-0321. Jurnal Teknologi Informasi (JTI) merupakan Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika yang menyajikan hasil penelitian yang fokus pada bidang informatika. Jurnal Teknologi Informasi (JTI) terbit dua kali dalam satu tahun (Januari dan Agustus). JTI ini fokus mempublikasi hasil penelitian orisinal yang belum diterbitkan di mana pun, isu yang dipublikasi oleh JTI meliputi pengembangan ilmu pengetahuan komputer dan informatika, fokus pada sains ilmu komputer, teknologi komputer tepat guna, dan rancang bangun sistem informasi.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 465 Documents
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SKEMA JARINGAN TROUBLESHOOT MENGGUNAKAN CISCO PACKET TRACER farwah, Farwah guzelim; Fauzul, Ahmad Fauzul Mubin; Tabrani, Ahmad Tabrani
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 19 No. 1 (2025): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v19i1.18768

Abstract

Penelitian ini bertujuan untuk mengidentifikasi dan mengatasi berbagai permasalahan jaringan komputer melalui pendekatan simulasi menggunakan Cisco Packet Tracer. Metode troubleshooting berbasis model OSI diterapkan untuk memecahkan skenario permasalahan seperti koneksi fisik yang terputus, kesalahan konfigurasi IP, kegagalan DHCP, konflik VLAN, dan masalah routing. Simulasi ini menunjukkan efektivitas Cisco Packet Tracer dalam memberikan solusi praktis dan interaktif terhadap tantangan jaringan modern. Penelitian ini juga memberikan panduan langkah-langkah troubleshooting yang sistematis, meningkatkan pemahaman pengguna tentang manajemen jaringan, dan mendukung proses pembelajaran interaktif di bidang teknologi informasi. Hasil penelitian memperlihatkan bahwa penggunaan Cisco Packet Tracer dapat meningkatkan efisiensi troubleshooting hingga 30% dan pemahaman konseptual hingga 45%. Dengan demikian, penelitian ini berkontribusi pada pengembangan keterampilan analitis dan teknis yang relevan dalam dunia pendidikan dan profesional.
OPTIMASI HASIL PERAMALAN BROWN’S WEIGHTED EXPONENTIAL MOVING AVERAGE MENGGUNAKAN METODE LEVENBERG-MARQUARDT (STUDI KASUS: HARGA BERAS DI KALIMANTAN TIMUR) Stevany Mbu, Margaretha; Purnamasari, Ika; Mahmuda , Siti
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 19 No. 1 (2025): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v19i1.19088

Abstract

Rice is a staple food for Indonesian people whose price often fluctuates. Throughout the period from January 2021 to September 2024, the price of rice in East Kalimantan continued to increase, forming a trend pattern. Data forecasting with a trend pattern can be done using the Brown's Weighted Exponential Moving Average (B-WEMA) method which uses smoothing, weighting, and order parameters in its calculations. In this method, a larger weighting is given to the latest data and the order used is a high order. In addition, determining the smoothing parameters that provide the best accuracy value is usually done by the trial-and-error method. To increase the accuracy value of the B-WEMA method, smoothing parameter optimization can be carried out using the Levenberg-Marquardt method. The purpose of this study was to apply the B-WEMA method with Levenberg-Marquardt optimization to rice price forecasting in East Kalimantan. The results showed that the smoothing parameters obtained by the B-WEMA method before optimization were 0,7 with an accurate value of MAPE is 0,820%. After optimization, the optimal smoothing parameter obtained was 0,652 which resulted in an accurate value of MAPE is 0,818%. This indicates an increase in the accuracy value. Based on the optimization results, the forecast results for the next 3 periods were obtained, namely Rp16.070 for October; Rp16.027 for November; and Rp15.985 for December.
PENERAPAN TEKNOLOGI AUGMENTED REALITY (AR) DALAM PEMBELAJARAN KOSA KATA BAHASA INGGRIS Bambang Sutiyo, Hestin Setiya Dianti; Muhamad Hadi Arfian
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 19 No. 1 (2025): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v19i1.19099

Abstract

Teknologi dalam dunia pendidikan sangat mempengaruhi minat belajar dibandingkan dengan belajar hanya dengan buku teks. Teknologi dapat meningkatkan pemahaman masyarakat terhadap kosakata bahasa Inggris, salah satunya adalah teknologi yang dikenal dengan Augmented Reality yang menghubungkan dunia digital dengan lingkungan pengguna secara real-time. Aplikasi pembelajaran ini diciptakan untuk mengatasi dampak negatif penggunaan smartphone dan mengajarkan kosakata bahasa Inggris sebagai pengenalan awal dalam pembelajaran. Media pembelajaran interaktif ini ditujukan untuk meningkatkan daya ingat pengguna dan meningkatkan fokus mereka terhadap layanan pembelajaran. Tidak hanya dapat memberikan informasi dan pengetahuan, tetapi juga dapat menghibur pengguna. Tujuan dari penelitian ini adalah untuk membuat aplikasi “Augmented Reality English Vocabulary Learning”, dengan menggunakan metode Multimedia Development Life Cycle (MDLC). Augmented Reality (AR) dapat menawarkan solusi yang inovatif, informatif, dan menarik untuk pembelajaran. Selain itu, teknologi ini memiliki kemampuan untuk menyajikan objek virtual dalam bentuk 3D secara real time.
IMPLEMENTASI ALGORITMA FIFO DAN DESCENDING PRIORITY QUEUE PADA SISTEM ANTRIAN PELAYANAN KESEHATAN PUSKESMAS BUNTOK Efrans Christian; Kristianti, Novera; Anugrahnu, Dian Putra; Putra, Putu Bagus Adidyana Anugrah; Pranatawijaya, Viktor Handrianus
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 19 No. 1 (2025): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v19i1.19246

Abstract

Healthcare services at Puskesmas Buntok often face challenges in managing patient queues, leading to long waiting times and decreased patient satisfaction. This study aims to design and implement an intelligent queue management system based on a website using FIFO (First In First Out) and Descending Priority Queue algorithms, as well as integrate wait time prediction using the Least Squares method in PHP Machine Learning. The software development methodology employed is the Waterfall model, encompassing requirement definition through flowchart creation, system and software design using UML (Use Case Diagram, Activity Diagram, and Class Diagram), implementation with PHP and MySQL, and system testing using black box testing and accuracy testing for the wait time prediction feature. The research results indicate that the developed intelligent queue system efficiently and effectively manages the order of patient services. The integration of wait time prediction provides accurate estimates, assists patients in planning their visits, and enhances the operational efficiency of the health center. System testing confirms that all functions operate as expected, making the website suitable for use as the official platform of Puskesmas Buntok. This implementation successfully improves the quality of healthcare services by reducing patient waiting times, increasing registration efficiency, and optimizing medical service processes through an innovative intelligent queue system
DETEKSI GERAK BERDASARKAN FITUR WAJAH MENGGUNAKAN METODE KANADE LUCAS TOMASI (KLT) Apridiansyah, Yovi; Marhalim; Fahmi, Nofear
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 19 No. 2 (2025): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v19i2.19848

Abstract

Research by utilizing facial recognition features related to image processing and computer vision is used to produce a system that is almost close to the human visual system in general. In image processing, the detection of the movement of the rig is carried out so as to produce detection results. A problem that often occurs in the motion detection process is that every moving object in the video will be detected as a moving object. Therefore, this study will try to detect human face objects from the video data to be detected so that the detection results will later produce the detection of face objects. Every process of observing human facial movements requires a careful pre-process stage, because it is related to the observation of very smooth movements and a very fast duration. At this stage, the detection and tracking of the facial area must always be precise so that the observation of movements made in the facial area can be accurate. The solution offered for facial motion detection is to apply the Canade Lucas Tomasi (KLT) method for tracking each feature point. The performance process of KLT in detecting faces is to track each existing face by looking at the point of facial features, after the system records the features of the face, the system will detect every facial movement in the video. So by using the KLT method, it is hoped that the system can detect facial objects in the video. The results of the study by testing as many as 30 samples of video data in the form of recordings of human motion objects succeeded in detecting facial movements with an accuracy level of 96%, Recal 88% and an accuracy level of 86%.
PENERAPAN SPATIAL DURBIN MODEL PADA DATA PENYAKIT MALARIA DI INDONESIA Nabilla, Maghrisa Ayu; Hayati, Memi Nor; Sifriyani, Sifriyani
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 19 No. 2 (2025): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v19i2.20334

Abstract

The Spatial Durbin Model (SDM) is a special case of the Spatial Autoregressive (SAR) model, involving the addition of spatial lag effects of both the dependent and independent variables. The parameter estimation used in this study is the maximum likelihood estimator. Parameter estimation for the SDM is performed at each observation location using spatial weighting. The spatial weights are calculated based on queen contiguity and customized contiguity weighting methods. This study aims to obtain the SDM and identify the factors influencing the number of malaria cases in Indonesia in 2023. The Lagrange Multiplier (LM) test indicates that there is a spatial lag in the dependent variable, with the parameter ρ being significant at a significance level of α = 0.1. Based on the results of the SDM analysis, it was found that the factors directly influencing the number of malaria cases in Indonesia in 2023 are the percentage of poor population, number of medical personnel and the percentage of households with access to adequate drinking water services. Meanwhile, the factors that have an indirect or spatial lag effect are the open unemployment rate and the percentage of poor population.
DETEKSI WARNA KULIT DAN REKOMENDASI PALET WARNA BERDASARKAN SEASONAL COLOR MENGGUNAKAN CNN Adzkia, Hawarizmi Ummul; Asriyanik, Asriyanik
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 19 No. 2 (2025): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v19i2.20597

Abstract

The Personal Color trend has grown significantly, assisting individuals in selecting clothing colors that complement their skin tone. This research aims to develop a system using Convolutional Neural Networks (CNN) to detect skin tones from photos and recommend color palettes based on Seasonal Color Theory. The system categorizes skin tones into the four seasonal types: Winter, Summer, Autumn, and Spring, and provides tailored clothing color suggestions. By applying machine learning, this system offers a personalized solution for clothing selection, enhancing the shopping experience. It aligns with the growing popularity of Personal Color trends, helping users make more confident and informed color choices that suit their individual characteristics.
OPTIMASI MODEL DETEKSI ALERGEN PADA PRODUK PANGAN DENGAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) DAN ADAPTIVE BOOSTING (ADABOOST) Siska Narulita; Sekarlangit; Milka Putri Novianingrum
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 19 No. 2 (2025): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v19i2.21316

Abstract

One important aspect that needs to be considered in food production is food safety. The implementation of this food safety aspect includes food products that avoid contamination of chemical, physical, and biological substances that can be harmful to human health. In the implementation of the Makan Bergizi Gratis (MBG) program, problems were found related to allergies in the recipients of this assistance program. According to the World Health Organization (WHO), food allergies are ranked as the fourth most serious public health problem, and the only effective treatment for allergy sufferers is to avoid foods that contain allergens. Allergens themselves are compounds or food ingredients that cause allergies and/or intolerances. Laboratory tests of food products for allergen testing that are still carried out traditionally require a lot of time and money, making food producers reluctant to carry out product testing. A way to detect allergen content in food products that is easier, more practical, and more accurate is needed. The research conducted aims to build a prediction model that can be used to detect allergen content in food ingredients through the implementation of the Support Vector Machine (SVM) data mining algorithm optimized with the Adaptive Boosting ensemble learning boosting algorithm (AdaBoost). The research conducted obtained a model that produces the most optimal performance, namely SVM optimized with the AdaBoost algorithm with the split validation method.
SOLAR TRACKING SYSTEM DENGAN ARDUINO NANO DAN SENSOR LDR: PANEL SURYA BERGERAK MENGIKUTI ARAH CAHAYA Fazilatunnisa, Azwa; Mochamad Yogi Febriansyah; Firdaus; Ahmad Fauzul Mubin; Daffa Wahid Sya'bani; Dony Hutabarat
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 19 No. 2 (2025): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v19i2.21746

Abstract

This study discusses the design of a single-axis light tracking system based on an Arduino Nano microcontroller. The system is designed to automatically direct solar panels to follow the direction of incoming light using two LDR sensors and one servo motor. The LDRs detect light intensity on the left and right sides, while the Arduino controls the comparison logic to move the servo according to the direction of the dominant light. Functional testing was conducted indoors using artificial light. Observation results indicate that the system responds well to light direction. The servo's movement aligns with changes in light intensity, though there is a response delay of approximately 1–2 seconds. The system also demonstrates stability when light intensity is balanced, indicating that the control logic operates effectively. The system's limitations lie in its horizontal movement range and the fact that it has not been tested outdoors. Nevertheless, this prototype can serve as a foundation for developing a two-axis tracking system or integrating it with the Internet of Things (IoT) for further applications.
ENSEMBLE MAJORITY VOTING UNTUK ANALISIS SENTIMEN DAN EMOSI PADA KOMENTAR YOUTUBE: STUDI KASUS RESIDENT EVIL 4 REMAKE Ahmad Abdul Hadi; Priskila, Ressa; Viktor Handrianus Pranatawijaya; Kristianti, Novera
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 19 No. 2 (2025): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v19i2.22397

Abstract

Currently, social media can be said to be one of the important things in the fields of marketing, broadcasting and entertainment, such as the gaming industry. In this case, Sentiment Analysis and Emotion Detection can be a tool for understanding the public's response and perception of the content presented. One of them is for the game Resident Evil 4 Remake, which was announced on March 24, 2023, and received a lot of public response on various social media platforms such as YouTube, one of which received responses in the form of 7177 comments between June 3 2022 and February 9, 2024. The research methodology used includes data collection methodology and simulation methodology, by combining the Naive Bayes algorithm, SVM and BERT using the Majority Voting method where these algorithms were previously trained using two different datasets which showed Naive Bayes performance with an accuracy of 84%, SVM with 89%, BERT with 93% and the Majority Voting Method with 90% accuracy with training using the Resident Evil 4 Remake dataset. And in training with the Steam Game Review dataset, Naive Bayes and SVM were obtained with an accuracy of 53%, BERT with 66%, and the Majority Voting Method with an accuracy of 57%. The Majority Voting classification model trained on the Resident Evil 4 dataset was used to perform Sentiment Analysis classification on comments from the YouTube video entitled "Resident Evil 4 Remake: Reveal Trailer" from the IGN Channel. The ratio of positive and negative sentiments was 60.2% and 39%. .8% with the frequency of emotions of anger, excitement and anticipation appearing most frequently.

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