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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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Optimalisasi Pelayanan Publik melalui Aplikasi SIPAKU di Kecamatan Kiaracondong Gilbert Aditya; Maharani Suryaningsih Imanto; Ardieansyah Ardieansyah
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.v5i1.5347

Abstract

The advancement of information technology has compelled government institutions to undergo digital transformation as a means of enhancing the effectiveness and efficiency of public services. As citizens increasingly demand fast, accessible, and transparent administrative processes, digitalization has become a necessary strategy. One such initiative is undertaken by the Kiaracondong Subdistrict through the development of SIPAKU (Integrated Administrative Services Information System), a digital platform designed to facilitate online civil administration services such as ID card applications, family registration, moving letters, and other administrative documents. This study aims to evaluate the extent to which SIPAKU has optimized public service delivery at the subdistrict level and to identify the key enabling and inhibiting factors affecting its implementation. The study applies a qualitative descriptive method with data collected through direct observation at the subdistrict office, informal interviews with civil servants, and documentation of the SIPAKU system in use. The findings reveal that SIPAKU has improved service accessibility for the public, accelerated administrative procedures, and enhanced bureaucratic transparency and accountability at the local level. However, the system also faces several challenges, including limited digital literacy among residents, inadequate technological infrastructure, and insufficient public outreach regarding the application’s use. This study recommends the full integration of SIPAKU with broader civil registration databases, intensive training for administrative personnel, and the development of inclusive communication strategies to ensure equitable access to digital services across all community groups.
Pemanfaatan Metode TOPSIS dalam Menentukan Rekomendasi Laptop Unggulan di Marketplace Tokopedia Pratiwi Susanti; Saifulloh Saifulloh; Alim Citra Aria Bima; Muh Nur Lutfi Aziz; Latjuba Sofyana STT
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.v5i1.5357

Abstract

The increasing public demand for laptop devices, particularly through marketplace platforms like Tokopedia, results in challenges when selecting a laptop that meets users' needs and preferences. Explore the key laptop specifications that are most important to consumers, such as battery life, RAM, and storage options. Discuss current market trends in laptop sales, including the most popular brands and models among users on platforms like Tokopedia. Highlight the importance of user reviews and testimonials in guiding potential buyers toward their ideal laptop choice. Provide tips for effective comparison shopping on marketplace platforms to help users narrow down their options. Analyze how different consumer preferences (e.g., gaming vs. productivity) influence the types of laptops that are in demand. This study aims to build a Decision Support System (DSS) for selecting the best laptop using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The study was conducted by identifying the main criteria, such as price, RAM, CPU, memory, and dimensions of the laptop, which were then used in the TOPSIS calculation process to determine the best alternative from 15 laptop choices. The results of the study show that the TOPSIS method is able to provide accurate and swift recommendations in choosing a laptop based on user preferences. We implement the system as a website, enabling users to input their preferences and receive automatic laptop recommendations. We expect this study to assist users in making laptop purchasing decisions more effectively and efficiently
Prediksi Preferensi Peserta Event Marathon terhadap Kategori Lomba menggunakan Algoritma Machine Learning Dominic Dinand Aristo; Satria Dwi Nurwicaksana; Dendi Putra Prakoso; M Afrian Maulana; Nurfaizah Nurfaizah
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.v5i1.5364

Abstract

Marathons are becoming an increasingly popular form of exercise and social interaction. Participants who choose race categories based on the mileage provided, such as 6K, 7.9K, and 11K, according to personal preference. However, this category selection has not been analyzed based on participant characteristics, even though this information is important for organizers to support promotional strategies, and segmentation of participants. This study aims to predict marathon category selection based on demographic characteristics, namely age and gender, by applying Decision Tree and Random Forest machine learning algorithms. The dataset used is primary data from two events, namely RSDK Berlari with a total of 1091 data and Skybridgefunrun with 1519 data. The results show that the Decision Tree algorithm gets an accuracy of 56.81%, and the Random Forest algorithm is 57.38%. With these results, it shows that the Random Forest algorithm has higher accuracy than the Decision Tree algorithm, with accuracy reaching 57.38%. However, the model tends to be biased towards the 7.9K category, with recall reaching 94%, while the 6K and 11K categories are very low. Then, feature importance analysis shows that the most influential factor on category selection is age, while gender is smaller. This research provides insight for event organizers in designing promotional strategies and participant segmentation more precisely.
Sistem Pemesanan Tiket Kapal Ferry Dengan Customer Relationship Management Berbasis Bot Telegram Linus Evrianus Ama Kean; Yohanes Suban Belutowe
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.v5i2.5371

Abstract

This research aims to design and develop a ferry ticket booking application integrated with Customer Relationship Management (CRM) based on a Telegram bot. The application is expected to facilitate customers in booking ferry tickets online and enable companies to manage customer relationships more effectively. The research method used is the waterfall method, which consists of the stages of analysis, design, implementation, testing, and maintenance. This application will be built using the Python programming language and the Telegram Bot API library for bot development. The main features of this application include ticket booking, customer data management, ferry data management, and two-way communication between customers and the company via the Telegram bot.
Sistem Pendukung Keputusan Penerimaan Bantuan Pangan Non-Tunai (BPNT) Menggunakan Metode Simple Multi-Attribute Rating Technique di Desa Kelapapati Muhammad Firqi Saputra; Ryci Rahmatil Fiska
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.v5i2.5378

Abstract

Social assistance is a form of government concern to alleviate poverty. This assistance can be in the form of money, clothing, food, or medicine needed by the community. The purpose of social assistance is to improve the welfare of the community. In providing social assistance, both the local government as the provider and the recipient community must be accountable according to the provisions. Social assistance is regulated in Government Regulation No. 58/2005 and Minister of Home Affairs Regulation No. 13/2006. Kelapapati Village faces challenges in the efficient distribution of social assistance. Manual processes in registration, data management, distribution, and monitoring often lead to problems such as recipient verification errors, misuse, and untargeted distribution. Subjectivity in assessing recipient eligibility also triggers social jealousy. The SMART (Simple Multi-Attribute Rating Technique) method is applied as a solution to improve efficiency. This method prioritizes beneficiaries based on criteria such as number of dependents, income, employment, and single parent status.
Perancangan Sistem Informasi Pemesanan Jasa Wedding Organizer Berbasis Web di Ranni Gallery Palembang Nabila Putri Mareta; Andreo Yudertha; Heru Kurniawan
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.v5i1.5390

Abstract

Ranni Gallery is a business engaged in the service sector, which provides wedding equipment. The business process at Ranni Gallery still uses a lot of manual methods causing inefficiency in ordering, it is not uncommon for schedules to clash, double bookings, and slow and error-prone price calculations. Manual ordering that requires customers to come directly causes an increase in costs and time efficiency because 60% of customers come from distant villages. The absence of monthly reports also made it difficult to monitor transactions and business development. A platform is needed to streamline Ranni Gallery's business, reduce customer dissatisfaction, and minimize errors in calculations, schedules, and monthly reports, namely with a wedding organizer service booking information system. The method used in developing this system is the waterfall waterfall model, using the PHP programming language laravel framework, and designing using the UML (Unifed Modeling Languange) model. Based on the results of blackbox testing, all functions in this system run as expected. Meanwhile, based on the results of UAT testing, this wedding organizer service booking information system obtained a percentage result of 93% which proved that this system was well received by users.
Sistem Kontrol Nutrisi Otomatis pada Tanaman Tomat Berbasis Arduino Sri Kurniyan Sari; Supriadi Sahibu; Zahir Zainuddin3; Fajar Husain A
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 4 No. 3 (2024): 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.v5i2.5393

Abstract

This research aims to develop an automatic nutrient delivery system for tomato plants in order to ensure optimal nutrient concentration and reduce manual labor during cultivation. The system is designed using a TDS sensor to measure nutrient concentration in water, a water level sensor to monitor the volume of solution in the nutrient container, and a relay module to control the activation of the nutrient pumps and circulation pump. The microcontroller processes sensor readings and regulates the addition of nutrients when the concentration falls below the preset threshold. Tests were conducted to evaluate sensor accuracy, pump responsiveness, and the system’s overall performance in maintaining nutrient stability. The results indicate that the water level sensor accurately detects changes in water volume, the TDS sensor successfully measures PPM values above 1400, and both nutrient pumps operate effectively to dispense nutrient solutions as required. The circulation pump also functions properly in distributing the nutrient-enriched water into the hydroponic pipes. These findings demonstrate that the proposed Arduino-based automatic nutrient system is capable of maintaining appropriate nutrient levels for tomato cultivation, supporting more efficient and consistent hydroponic plant growth.
Implementasi Convolutional Neural Network (CNN) untuk Klasifikasi Citra Batik Nusantara Zufar Faiil Haq; Mufti Ari Bianto; Afifah Agustin; Moch. Ryan Nurfebrianto
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.v5i1.5421

Abstract

Batik is a cultural heritage of the nation, with each batik having a unique and diverse pattern motif. The batik culture is very strong in Indonesia, so batik can be found in all regions of the archipelago. Each batik has its own characteristics and traits to distinguish itself in each area. However, many people find it difficult to differentiate the types of batik motif patterns, one of which is the Nusantara Megamendung batik. Therefore, this research aims to introduce the classification process of Nusantara batik motif patterns using one of the Deep Learning methods, namely Convolutional Neural Network (CNN), to differentiate the types of batik motif patterns in each region. The dataset is taken from the numeric representations of Red, Green, and Blue (RGB) values of each pixel, which are used as model learning features to study color patterns and textures. From the results of the experiments conducted, the batik image classification using the CNN method has a high level of accuracy The batik classification model achieved an accuracy of 85%, demonstrating a fairly good ability to identify batik images, one of which is the Mega Mendung batik. The Mega Mendung and Keraton classes showed perfect performance, with precision, recall, and F1-score close to 1.00. However, the Bali class was the main weak point, with a recall of only 60%, indicating that 40% of Bali Batik samples were misclassified, primarily as Keraton.
Analisis Sentimen Opini Publik pada Channel Youtube Mata Najwa Menggunakan Metode SVM Asmara Andhini; Fadilah Nuria Handayani; Intan Diasih; Nurmalitasari
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.v5i2.5426

Abstract

The rapid development of social media, particularly the YouTube platform, has created an active and open space for public discourse. One prominent example is the program "Mata Najwa", which frequently discusses important societal issues. The episode titled "Retno Marsudi & Sri Mulyani: Women in Power Mata Najwa" garnered significant attention, sparking a variety of responses from netizens in the comments section. This study aims to explore public sentiment toward female leadership by utilizing the Support Vector Machine (SVM) classification method. A total of 4,626 comments from Najwa Shihab’s YouTube channel on the aforementioned episode were analyzed through several stages, including data preprocessing, sentiment labeling using a lexicon-based approach, feature extraction via the TF-IDF method, and classification using the SVM algorithm. The model evaluation demonstrated excellent performance, with an accuracy of 95.36%, precision of 95.70%, recall of 95.36%, and an F1-score of 95.27%. The model accurately identified positive and neutral comments but showed a limitation in detecting negative comments, likely due to class imbalance. This study offers new insights into public perceptions in digital spaces and reaffirms the effectiveness of SVM in text-based sentiment analysis.
Deteksi Penyakit Daun Terong Menggunakan MobileNetV2 Hardy Gustino; Muhammad Rafi Winno Pratama; Rafli Aldrian Kurnianto; Anggraini Puspita Sari
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.v5i2.5433

Abstract

Eggplant is a horticultural crop that is highly dependent on the health of its leaves to support growth and productivity. Leaf diseases can cause a significant reduction in crop yield if not detected early. This study aims to develop a leaf classification model for eggplant using the MobileNetV2 architecture to automatically detect leaf conditions. The model was trained using a public dataset of eggplant leaf images, with an 80% training and 20% validation data split. During the twenty-epoch training process, the model achieved a validation accuracy of 93%. The final model is stored in a lightweight format. The results of this study indicate that this approach is effective for detecting diseases in eggplant leaves and has the potential to support the implementation of responsive smart agriculture in the field.

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