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Jurnal Teknik Informatika dan Teknologi Informasi
Published by Lembaga Pengembangan Kinerja Dosen
ISSN : 28279379     EISSN : 28279387     DOI : 10.55606
Core Subject : Science,
Computer Science & IT
Jurnal Teknik Informatika dan Teknik Informasi (JUTITI) adalah jurnal ilmiah peer review yang diterbitkan oleh Politeknik Pratama. Jurnal Teknik Informatika dan Teknik Informasi (JUTITI) terbit dalam tiga edisi dalam setahun, yaitu edisi Februari, Juni dan Oktober. Kontributor Jurnal Teknik Informatika dan Teknik Informasi (JUTITI) berasal dari peneliti, akademisi (dosen dan mahasiswa) di bidang Teknik Informatika dan Teknik Informasi. Jurnal Teknik Informatika dan Teknik Informasi. memiliki fokus dan ruang lingkup yang terdiri dari: Computer Architecture Parallel and Distributed Computer Pervasive Computing Computer Network Embedded System Human—Computer Interaction Virtual/Augmented Reality Computer Security Software Engineering (Software: Lifecycle, Management, Engineering Process, Engineering Tools and Methods) Programming (Programming Methodology and Paradigm) Data Engineering (Data and Knowledge level Modeling, Information Management (DB) practices, Knowledge Based Management System, Knowledge Discovery in Data) Network Traffic Modeling
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 197 Documents
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Pengaruh TIngkat Skala Keabuan Terhadap Akurasi Klasifikasi Jenis Ikan Melalui Citra Sisik Ikan Menggunakan Jaringan Syaraf Tiruan Gilang Hadi Ramadhan; Gasim Gasim; Mustafa Ramadhan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5796

Abstract

This study was conducted to examine the effect of grayscale image variations on the accuracy of fish species recognition by utilizing fish scale images through the Artificial Neural Network (ANN) method. Automatic fish species identification plays a crucial role in the fisheries sector, both for research purposes, marine resource monitoring, and trade processes. One factor that can influence recognition accuracy is the quality of image representation, including the grayscale level used. Therefore, this study aims to analyze how much grayscale level variations affect fish species classification results. This research method uses a dataset consisting of 180 scale images for each fish species. Of these, 150 images are used as training data and 30 images as test data. The feature extraction process is carried out using the Gray Level Co-occurrence Matrix (GLCM) method, which utilizes contrast, energy, homogeneity, correlation, and entropy parameters. These features are then used as input to the ANN for the classification process. The analysis was conducted by comparing the accuracy results of various grayscale levels, namely 16, 32, 64, 128, and 256 levels. The results showed that variations in grayscale significantly influenced the accuracy level of fish species recognition. The highest accuracy was obtained at a scale of 256 levels with a value of 96%, followed by a scale of 128 levels at 95%, 64 levels at 92.5%, 32 levels at 84.2%, and the lowest at 16 levels with an accuracy of only 82.5%. In conclusion, the higher the variation in grayscale levels used, the better the recognition accuracy obtained. Thus, the use of images with 256 grayscale levels is recommended for research on fish scale image classification using the ANN method because it is able to provide the most optimal results.
Perbandingan Kinerja Algoritma Support Vector Machine dan Random Forest dalam Analisis Sentimen Ulasan Hotel di Kota Palembang pada Google Maps Ari wiyanto; Shinta Puspasari; Lastri Widya Astuti
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5802

Abstract

The growth of the tourism sector in Palembang City has encouraged an increase in the need for quality hospitality services. In the digital age, user reviews on the Google Maps platform are an important source of data to assess customer satisfaction. This study aims to analyze and compare the effectiveness of two sentiment classification algorithms, namely Support Vector Machine (SVM) and Random Forest, in processing hotel reviews in Indonesian. A total of 1000 review data was used and processed through the stages of text cleanup, letter normalization, tokenization, stopword removal, and stemming. The evaluation was carried out with two approaches: 80:20 data sharing and cross-validation using the K-Fold technique. On data sharing, Random Forest showed 88% accuracy and 100% recall, while SVM recorded 87% accuracy and 99% recall, with equivalent precision and F1-score. However, cross-validation showed that the SVM was more stable and consistent, with 92% accuracy, 94% accuracy, 98% recall, and 96% F1-score, outpacing Random Forest's 91% accuracy and 95% F1-score. These results show that the SVM algorithm is superior in analyzing hotel review sentiment on Google Maps. These findings provide recommendations for tourism information system developers to adopt an SVM-based approach to review data processing to support more accurate and responsive decision-making.
Media Pembelajaran Digital Berbasis Scratch sebagai Inovasi Pembelajaran di TK Gracia Waharia Nabire Kristia Yuliawan; Wisna Bara’ Padalingan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5821

Abstract

This research aims to develop a Scratch-based digital learning media called Rimba Ceria, intended for early childhood at Gracia Waharia Kindergarten, Nabire, Papua. The background of this activity departs from the limited educational resources and the low involvement of children in the learning process in the 3T (disadvantaged, frontier, and outermost) areas. Rimba Ceria media is designed in an interactive and interesting manner, in accordance with the characteristics of early childhood learning which are visual and game-based. The development process uses the ADDIE model approach which includes the stages of Analysis, Design, Development, Implementation, and Evaluation. The results of the development show that this medium successfully integrates text, audio, video, and interactive elements to introduce the categories of terrestrial, marine, and aerial animals. This innovation not only fills the gap in relevant learning media in remote areas, but also contributes to improved computational thinking, problem-solving skills, creativity, and early childhood learning engagement. Evaluation shows that this media is well received by students and teachers, and has the potential to be replicated in similar areas. Thus, Rimba Ceria is an adaptive and applicative educational solution in supporting the equitable distribution of the quality of early childhood education, especially in areas with limited access to technology and learning resources.
Komparasi Metode Support Vector Machine dan Random Forest untuk Prediksi Penjualan Solar Industri (HSD) pada PT Heva Petroleum Energi Palembang Putri Octaria; Shinta Puspasari; Evi Purnamasari
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5829

Abstract

The fluctuating nature of Industrial Solar or High Speed ​​Diesel (HSD) sales poses a significant challenge for companies, particularly in developing appropriate distribution strategies and stock planning. This situation demands the application of data-driven analytical methods to support more effective decision-making. This study aims to predict Industrial Solar sales at PT Heva Petroleum Energi Palembang using two Machine Learning methods, namely Support Vector Machine (SVM) and Random Forest. The data used are monthly sales records for the period 2022–2024. The research process includes data collection, pre-processing with normalization and feature selection, model building, testing by dividing the data into training and test sets, and performance evaluation using the Mean Absolute Percentage Error (MAPE) metric. The results show that the Random Forest model produces a MAPE value of 12.48%, while the Support Vector Machine model obtains a MAPE value of 12.97%. This comparison shows that Random Forest is superior in predicting sales compared to SVM. Thus, it can be concluded that Random Forest is a more appropriate choice for application in modeling Industrial Solar sales. The implications of these findings are expected to provide a real contribution to companies in developing distribution policies and stock management that are more accurate, efficient, and sustainable, so as to be able to support the stability of company operations in the future.
Analisis Sentimen Kepuasan Pengguna Lintas Rel Terpadu (LRT) menggunakan Metode Support Vector Machine Rangga Febri Kasih; Rendra Gustriansyah; Zaid Romegar Mair
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5832

Abstract

This study aims to analyze public sentiment toward the Palembang LRT service by utilizing user reviews available on the Google Maps platform. Sentiment analysis was conducted to understand public perceptions of service quality, which can serve as a basis for decision-making in improving public transportation services. The method employed in this research is the Support Vector Machine (SVM) algorithm combined with Term Frequency-Inverse Document Frequency (TF-IDF) for word weighting, which classifies reviews into two sentiment categories: positive and negative. A total of 500 reviews were randomly selected as the dataset and processed through a text preprocessing stage, including data cleaning, tokenization, and stopword removal to enhance data quality. The SVM model was then evaluated using an 80:20 split for training and testing, achieving an accuracy of 91%, which indicates excellent performance in identifying sentiment patterns in the Indonesian language. The findings of this study confirm that SVM-based approaches are effective and reliable for sentiment analysis in the context of public transportation. These results provide practical contributions for Palembang LRT management, as insights into public sentiment can be used as a strategic reference for decision-making, reputation management, and improving service quality based on user needs. Future research is recommended to expand the dataset, include neutral sentiment categories, and compare SVM performance with other machine learning algorithms to achieve more comprehensive and robust results.
Analisis Penghematan Penggunaan Energi Listrik Berbasis Fuzzy pada Hotel Rifqi Wibowo; Sri Artini Dwi Prasetyowati; Agus Adhi Nugroho
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5839

Abstract

Electricity is an important necessity, but its use must be managed wisely to avoid waste and negative environmental impacts. Energy-saving programs implemented in buildings encourage consumers to use energy more efficiently, reduce wasteful consumption that leads to inflated electricity bills, and serve as an initial step in overall energy management to lower total energy costs. Hotel Grand Dian Bumiayu showed an initial IKE of 27.77 kWh/m²/year. This figure, with a total energy consumption of 144,752.01 kWh/year, falls into the highly efficient category. This means the building's energy usage is already considered optimal compared to existing standards. Fuzzification calculations, using data from September 2023, yielded a DeFuzzification value of 12.858. This value is within the IKE range for the "Moderately Efficient" criterion (12.08 – 19.17), meaning that, based on this analysis, energy usage during that period was classified as moderately efficient. A detailed energy audit identified four types of loads that significantly contributed to the hotel's power consumption. From this analysis, it was revealed that the largest electricity consumption (kWh/year) originated from the air conditioning system, accounting for 77%. Following the air conditioning system, the lighting system ranked second with 14% of the total consumption. Then, office equipment contributed 5%, and the smallest electricity consumption was recorded for utilities, at 4%. After implementing Energy Saving Opportunities (PHE), the final Energy Consumption Intensity (IKE) was recorded at 30,013.75 kWh/m²/year. This IKE value indicates that energy consumption is highly efficient. This result is quite reasonable, considering that the initial IKE even before the analysis of energy-saving opportunities was conducted, was already in the efficient category.
Pengembangan Aplikasi Augmented Reality Berbasis Android dalam Pembelajaran Geometri Bangun Ruang di Sekolah Dasar Putri Shabira Pratiwi; Shinta Puspasari; Mustafa Ramadhan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5853

Abstract

This research aims to develop an Augmented Reality (AR) application for Android devices to support the teaching of three-dimensional geometry in primary education. The development process employed the Multimedia Development Life Cycle (MDLC) model, which consists of six stages: concept, design, material gathering, assembly, testing, and distribution. The application is designed to display three-dimensional representations of basic geometric figures such as cubes, cuboids, prisms, pyramids, cylinders, cones, and spheres. In addition to visual models, the application also provides related mathematical equations and explanatory commentary to strengthen students’ conceptual understanding. The testing phase demonstrated that the AR application is capable of presenting 3D objects with clarity and stability when viewed from a distance of 10–30 cm and within an angle range of 45° to 135°. These conditions ensure that the objects remain easily recognizable and interactive in classroom learning environments. User feedback from both teachers and students highlighted the engaging nature of the application, particularly in fostering motivation, improving visualization skills, and encouraging interactive learning experiences. Overall, the findings suggest that this AR-based application can serve as an effective educational tool for primary school students, bridging the gap between abstract geometry concepts and practical visualization. By integrating modern technology into mathematics instruction, the application has the potential to enhance both comprehension and interest in learning geometry while supporting innovative digital learning practices in the classroom.
Pengaruh Tingkat Pencahayaan Pemotretan Urat Daun terhadap Tingkat Akurasi Pengenalan Jenis Bibit Mangga Menggunakan Metode Pengenalan JST-PB dan Fitur LBP Suci Aulia Ramadhani; Gasim Gasim; Mustafa Ramadhan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 1 (2025): April: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i1.5854

Abstract

Mango (Mangifera indica L.) is one of the most important tropical fruits with high nutritional value and significant economic potential. However, manual identification of mango seedlings remains less accurate due to the similarities in leaf shape and size among different varieties, which often leads to misclassification. This study aims to develop an automated system to recognize five types of mango seedlings—Harum Manis, Indramayu, Golek, Madu, and Gedong Gincu by utilizing leaf vein textures as the main distinguishing features. The methodology employed the Local Binary Pattern (LBP) technique for feature extraction and a Backpropagation Neural Network (BPNN) as the classification model. The dataset consisted of 250 training images and 125 testing images with a resolution of 100×100 pixels, captured under varying lighting conditions ranging from one to five lamps. The experimental results indicate that lighting conditions significantly affect classification accuracy. The highest accuracy was achieved under four-lamp lighting conditions, reaching 91.20%, followed by two lamps (89.60%), three lamps (87.20%), five lamps (76.80%), and one lamp (67.20%). Furthermore, a BPNN configuration with 12 hidden neurons consistently demonstrated reliable recognition performance. These findings suggest that the combination of LBP and BPNN is effective for automatic classification of mango seedlings. The implementation of this system has the potential to assist farmers and seedling institutions by improving efficiency, accuracy, and reliability in seedling identification, thereby supporting the advancement of technology-based agriculture.
The Analisis Kinerja dan Akurasi Sensor Thermocouple Tipe K dalam Sistem Pengendalian Suhu Reflow Soldering Purbo Tri Prakoso; Bustanul Arifin; Suryani ALifah
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5867

Abstract

The reflow soldering process is an important stage in the assembly of electronic components that requires high-precision temperature control to ensure the quality of the solder joints. This study aims to evaluate the performance of a K-type thermocouple temperature sensor integrated into the solder reflow control system. Evaluation was carried out through temperature measurements at 20 different points, ranging from 35°C to 220°C, with a focus on reading accuracy and error rate. The results of the experiment showed that the type K thermocouple sensor had an error range between 0.02°C to 0.97°C, with an average error value of 0.54°C.  These findings indicate that the sensor is stable and reliable enough to be used in industrial applications that demand temperature precision. The advantages of these sensors lie in their cost efficiency, ease of integration, and responsiveness to temperature changes. Nonetheless, periodic calibration is still necessary to maintain long-term accuracy, especially in dynamic work environments. This research contributes to the development of temperature control systems in the electronic manufacturing process, especially in the selection of the right sensors to support production quality. The conclusion of this study confirms that the K-type thermocouple is a practical and economical solution in reflow soldering systems, with sufficient performance to meet industry standards.
Pengembangan Sistem Informasi Geografis Penerangan Jalan Umum Kota Pontianak dengan Integrasi Telegram Bot API Steven Pragestu; Juanda Astarani
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6068

Abstract

Public Street Lighting (PJU) plays a crucial role in supporting community activities at night while ensuring the safety, comfort, and aesthetics of urban environments. However, in many regions, including Pontianak City, numerous streetlights remain non-functional for extended periods due to delayed damage reporting and the lack of an efficient monitoring system. This research aims to create a Public Street Lighting information system based on Geographic Information System (GIS) technology, integrated with the Telegram Bot API, to enhance the efficiency of damage reporting and response management. The system allows both the public to submit reports of malfunctioning lights through a web-based application, where the backend automatically sends notifications to administrators and technicians via Telegram. Data were collected through spatial data mapping, documentation, and interviews with the Head of the Road Equipment Division at the Pontianak City Transportation Department, followed by system design using the Waterfall model and testing through blackbox testing. The system visualizes the distribution and condition of streetlights on an interactive WebGIS map and records damage reports in real-time. The findings indicate that the integration of WebGIS and Telegram Bot API enhances the responsiveness and transparency of streetlight maintenance management. This innovation contributes to the realization of a smart city ecosystem by promoting community participation, improving infrastructure service efficiency, and supporting data-driven decision-making in local governance.

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