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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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Komparasi Algoritma Machine Learning (Random Forest, Gradient Boosting, dan Ada Boosting) untuk Prediksi Tingkat Penyakit Alzheimer Muhammad Raviansyah; Andika Amansyah; Farhan Fadhilah; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
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.v5i3.6227

Abstract

Alzheimer’s disease is one of the most common forms of progressive dementia and has become a major global health challenge as the aging population continues to increase. Early detection of this disease is crucial to mitigate its social, economic, and health impacts. In this context, data-driven approaches using machine learning algorithms can be utilized to predict Alzheimer’s risk more accurately. This study aims to compare the performance of three ensemble learning algorithms—Gradient Boosting, Random Forest, and AdaBoost—in predicting the risk level of Alzheimer’s disease using the public Alzheimer’s Disease Dataset, which includes demographic, clinical, and lifestyle data. The research process involved several stages, including data preprocessing, splitting data into training and testing sets, model training using cross-validation, and performance evaluation based on accuracy, precision, recall, F1-score, and AUC metrics. The experimental results show that the Gradient Boosting algorithm achieved the best performance with an accuracy of 0.956, an F1-score of 0.956, and an AUC of 0.985, demonstrating its ability to capture complex non-linear relationships among features such as age, MMSE score, and lifestyle factors. Meanwhile, Random Forest and AdaBoost achieved competitive yet slightly lower performance. These findings indicate that ensemble boosting approaches, particularly Gradient Boosting, hold great potential for medical decision-support systems in the early detection of Alzheimer’s disease and can serve as a foundation for developing more accurate and adaptive predictive models in the future.
Analisis Pola Pergerakan dan Prediksi Harga Emas Menggunakan Regresi Linear serta Model Time Series ARIMA dan VAR Roni Saputra Pratama; Ryehan Alfiansyah; Prasetyo Adi Suwignyo; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
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.v5i3.6233

Abstract

Gold is one of the most popular investment instruments due to its stable value and ability to protect assets against inflation. However, its price tends to fluctuate significantly, influenced by macroeconomic factors such as exchange rates, interest rates, and global geopolitical conditions. This study aims to analyze the movement patterns and predict gold prices based on historical data from 2019 to 2024 using the Linear Regression method and Time Series models, namely ARIMA and VAR. The analysis process was carried out using Orange Data Mining software, which enables the application of machine learning algorithms through a visual and interactive interface without manual coding. The dataset used consists of daily gold closing prices, processed and tested to evaluate model accuracy using Root Mean Square Error (RMSE) and Correlation Coefficient (R) indicators. The results indicate that the Linear Regression model effectively captures the general trend of gold prices, while ARIMA and VAR models produce more accurate forecasts based on historical fluctuations. The integration of regression and time series approaches improves prediction reliability. Overall, this research contributes to the development of financial data analysis and provides insights for investors in making more informed and data-driven investment decisions.
Penerapan Algoritma K-Means untuk Pengelompokan Kerentanan Wilayah terhadap Kasus DBD di Kota Bandung Zahwa Asfa Rabbani; Alya Avisa; Paulus Paulus; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
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.v5i3.6239

Abstract

Dengue Hemorrhagic Fever (DHF) is an infectious disease caused by the dengue virus and transmitted through bites of the Aedes aegypti mosquito. This illness remains a major public health concern in Indonesia, particularly in urban regions like Bandung City, where population density and environmental variations contribute to disease transmission. The purpose of this study is to apply the K-Means Clustering algorithm to group areas based on their level of vulnerability to DHF spread in Bandung City. The dataset, obtained from the Bandung Open Data portal covering the 2016–2024 period, was processed using the Orange Data Mining application. The analysis began with data preprocessing, which included cleaning, attribute selection, and normalization to ensure optimal clustering performance. The data were then grouped into three primary clusters representing high, medium, and low risk zones. The findings indicate that the K-Means algorithm effectively detects the spatial and temporal distribution of DHF cases and presents it through scatter plot visualizations that illustrate yearly patterns. High-risk regions are typically characterized by dense population, poor sanitation, and limited environmental management. These findings provide essential insight for local health authorities to design more targeted prevention and control strategies. Furthermore, this research can serve as a foundation for developing a decision support system that aids in monitoring, evaluating prevention efforts, and optimizing health resource allocation to reduce the incidence of DHF in the future.
Perancangan dan Implementasi Pelayanan Puskesmas Pembantu (Pustu) di Desa Kali Cinta Berbasis web Takeru Kakizaki; Yulina Yulina
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.v5i3.6243

Abstract

Improving the quality of basic health services is a priority, driving innovation at the Kali Cinta Village Community Health Center (Pustu). This Pustu still faces significant operational challenges, including long queues, inaccessible service information, and manual registration and medical record processes. These conditions result in suboptimal patient data management, frequent data duplication, and slow service delivery, ultimately reducing the efficiency and quality of Pustu services. This study aims to design and implement a computerized web-based Pustu service system. Data collection was conducted through direct observation of service flows, interviews with Pustu staff and the community to understand needs, and a literature review as a theoretical basis. Software development was conducted systematically using the Waterfall method. The expected findings from the implementation of this system are better access to information, accelerated registration processes, and efficient patient data management. The implications of this system are a significant improvement in the quality of health services and community satisfaction in Kali Cinta Village.
Sistem Informasi Inventaris Barang Berbasis Web pada Kelurahan Kotabumi Pasar Lampung Utara Leni Maryana; Yulina Yulina
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.v5i3.6245

Abstract

This research aims to design and implement a Web-Based Goods Inventory Information System at Kotabumi Pasar Village, North Lampung, to address the challenges associated with manual data collection and asset management. The system was developed using the SDLC Waterfall method, with the PHP programming language and MySQL database. It is designed to provide an intuitive interface and robust functionality, including features for adding, editing, deleting, and searching inventory data, as well as generating automatic reports. By automating these processes, the system seeks to improve the efficiency and accuracy of goods inventory management, reducing the risk of human error and increasing transparency in asset tracking. Additionally, the system is expected to enhance the village administration’s ability to monitor and make informed decisions about asset management, ultimately supporting better organizational performance and resource allocation. This study demonstrates the value of adopting digital solutions to modernize administrative tasks in local government settings, fostering more effective and sustainable governance practices.
Efektivitas Pelayanan Pendaftaran Diklat Berbasis AIRIS (Aplication Integrated Registration System) terhadap Kepuasan Peserta Diklat di Poltekpel Banten Abdul Hamid Arribathi; Sriyono Sriyono
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.v5i3.6251

Abstract

Digital transformation in public services is an effort to improve efficiency, transparency, and quality of service. Banten Maritime Polytechnic (Poltekpel Banten) as a Public Service Agency (BLU) has implemented the AIRIS (Application Integrated Registration System) online registration system to support the recruitment process of training participants, both new training participants and those upgrading their diplomas or certificates of seafaring skills training. The research method used is descriptive quantitative with data collection through questionnaires distributed to training participants who have used the AIRIS system. Data analysis was carried out using a Likert scale approach and effectiveness tests based on indicators of system quality, information quality, and service quality. The results of the study show that the implementation of AIRIS has a positive impact on increasing the efficiency of the registration process, data transparency, and ease of access to information for participants. Most respondents expressed a high level of satisfaction with the ease of use and speed of service provided by the system. However, several technical challenges, such as network disruptions and limited user guides for new users, were still encountered. Overall, AIRIS was deemed effective in improving the quality of training registration services and contributing to increased participant satisfaction. Therefore, it is recommended that it be further developed through feature optimization and increased digital infrastructure capacity.
Analisis dan Perancangan Sistem Informasi Pelayanan Pelanggan Berbasis Web pada King Kuphi Arfah Anggina; Zul Fadli Lubis
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.v5i3.6253

Abstract

The development of information technology has had a significant impact on improving service quality in various sectors, including the culinary industry. KingKuphi, as a culinary business, still uses a manual system for ordering and managing customer data, resulting in various obstacles such as service delays and recording errors. This research aims to design a web-based customer service information system that can automate the ordering process, menu management, and payment transactions in an integrated manner. The development method used is the System Development Life Cycle (SDLC) with the Waterfall model, which includes the stages of requirements analysis, design, implementation, verification, and maintenance. The results show that the designed system can improve efficiency and accuracy in service management by providing digital menu features, automatic ordering, order notifications, and digital receipt printing. With the implementation of this system, the service process becomes faster, more structured, and more integrated between customers, waiters, the kitchen, and the cashier. This information system is expected to improve service quality and customer satisfaction, while supporting KingKuphi's operational management in a more modern and efficient manner.
Penerapan dan Perbandingan Algoritma SVM, Naive Bayes, dan Gradient Boosting dalam Prediksi Stroke Joseph Melchior Nababan; Iqro Mukti Arto; Putra Satria; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
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.v5i3.6254

Abstract

Stroke is a major cardiovascular disease that significantly contributes to global mortality and disability rates. Early detection through stroke risk prediction is essential in reducing its impact. This study focuses on evaluating and comparing the performance of three machine learning algorithms—Support Vector Machine (SVM), Naive Bayes (NB), and Gradient Boosting (GB)—in predicting stroke occurrence. The research utilizes the Healthcare Stroke Dataset, which contains 5,109 records and 11 predictor variables. Modeling was performed using Orange Data Mining software, with 70% of the data allocated for training and 30% for testing. The results show that the SVM algorithm achieved the highest performance, obtaining an AUC score of 0.919 and an accuracy of 96.0%, followed by Gradient Boosting with an AUC of 0.885 and accuracy of 95.2%, and Naive Bayes with an AUC of 0.803 and accuracy of 88.2%. Therefore, SVM is identified as the most effective algorithm for predicting stroke risk within this dataset.
Pembuatan Game Visual Novel “One Year Of Time” pada Platform Website Jonathan Suryadi; Darius Andana Haris; Jeanny Pragantha
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.v5i3.6279

Abstract

The game “One Year of Time” is a form of interactive entertainment that engages players in rule-based activities with specific goals. Besides recreation, games also help develop critical thinking, problem-solving, strategic planning, and hand-eye coordination skills. “One Year of Time” is a 2D romance visual novel developed using Unity and programmed in C# for the web platform. Players take on the role of a god named Aion, who is descended to the human world due to a violation of the laws imposed by Zeus and is punished with a one-year time limit to find a partner—or face extinction. Along the way, players meet three main female characters, each with a unique personality. Each choice they make affects their relationships and determines the outcome of the story. Through these decisions and challenges, players explore the meaning of love, human relationships, and the consequences of their actions. Testing of this game was conducted. The game was tested using three methods: black box testing, an alpha test conducted by a supervisor, and a beta test conducted by 33 participants. The test results indicated that "One Year of Time" was considered engaging, easy to understand, and potentially well-received by players. This demonstrates the game's entertainment value and replayability. Therefore, it can be concluded that the game successfully provided a positive gaming experience and has potential for further development.
Klastering Penyakit Diabetes Melitus dengan Algoritma K-Means berdasarkan Karakteristik Klinis Audy Aulia Azzahra; Fajar Yoga Adiansyah; Erlangga Rizki Ekaptra; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
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.v5i3.6281

Abstract

Diabetes Mellitus is a complex and progressive chronic metabolic disorder that requires a personalized management strategy tailored to each individual’s clinical, physiological, and lifestyle characteristics. Addressing this challenge, the present study aims to apply the K-Means algorithm to identify clustering patterns among diabetic patients using the Knowledge Discovery in Databases (KDD) framework. The dataset was obtained from the Kaggle repository, consisting of 769 patient medical records with key variables such as glucose levels, body mass index (BMI), blood pressure, age, and other metabolic parameters relevant to the diagnosis of Diabetes Mellitus. The research methodology includes several stages: data selection, preprocessing to handle missing values, duplication, and normalization to ensure the dataset is properly structured for analysis. The implementation of the K-Means algorithm was carried out using Orange Data Mining software to produce optimal clustering patterns. The analysis identified three primary clusters (C1, C2, C3) that demonstrated significant differences, particularly based on glucose levels as the dominant variable in cluster formation. The scatter plot visualization revealed clear separations among clusters, with high intra-cluster homogeneity and strong inter-cluster heterogeneity. These findings confirm the effectiveness of the K-Means algorithm as an unsupervised learning method capable of uncovering hidden patterns within clinical diabetes data. The results are expected to serve as a foundation for developing more adaptive and precise clinical decision support systems, assisting healthcare professionals in designing targeted management and intervention strategies aligned with each patient’s risk profile.       

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