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All Journal JUSIFO : Jurnal Sistem Informasi Sistemasi: Jurnal Sistem Informasi Tech-E RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Pilar Nusa Mandiri JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) Technomedia Journal Amerta Nutrition Jurnal Sistem Cerdas JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CCIT (Creative Communication and Innovative Technology) Journal Journal of Social Science Jurnal Teknik Informatika (JUTIF) ADI Pengabdian kepada Masyarakat Jurnal (ADIMAS Jurnal) Jurnal Abmas Negeri (JAGRI) Naratif : Jurnal Nasional Riset, Aplikasi dan Teknik Informatika Green Intelligent Systems and Applications Jurnal Algoritma Journal of Social Science IWJ : Inland Waterways Journal Information Technology International Journal (ITIJ) The Indonesian Journal of Computer Science Indonesia Berdampak: Jurnal Pengabdian Kepada Masyarakat
Nouval Trezandy Lapatta
Universitas Tadulako
Religion Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Land Cover Analysis with Fully Convolutional Network Ihwan, Abib Raifmuaffah; Lapatta, Nouval Trezandy; Joefrie, Yuri Yudhaswana; Anshori, Yusuf; Syahrullah, Syahrullah
Jurnal Sistem Cerdas Vol. 8 No. 1 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i1.496

Abstract

This study analyses land cover in Morowali Regency using Sentinel-2 satellite imagery and the Fully Convolutional Network (FCN) algorithm. Land cover analysis in this area is crucial for monitoring rapid industrialization, especially in the mining sector. The methodology includes retrieving image data from Google Earth Engine, image processing to eliminate cloud influences, and model training using the European Space Agency (ESA) datasets. The results of the analysis show that 50% of the Morowali Regency area has the potential to be planted with trees, followed by 20% for water areas, and the rest for bushes, development land, and empty land. This study proves that FCN can be relied on to predict land potential with high accuracy with a loss value of 1.3001.
Utilization of EfficientNet-B0 to Identify Oncomelania Hupensis Lindoensis as a Schistosomiasis Host Lamadjido, Moh. Raihan Dirga Putra; Laila, Rahmah; Pusadan, Mohammad Yazdi; Yudhaswana, Yuri; Lapatta, Nouval Trezandy; Ngemba, Hajra Rasmita
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9058

Abstract

Schistosomiasis caused by the Schistosoma japonicum worm is a significant health problem in Indonesia, especially in endemic areas such as the Napu Plateau and Bada Plateau. The main problem in controlling this disease is the difficulty in rapid and accurate identification of Oncomelania hupensis lindoensis snails as intermediate hosts of the parasite. This research aims to develop an artificial intelligence-based system that can efficiently identify the snail species. The stages of this research include collecting snail image data from the Central Sulawesi Provincial Health Office, consisting of 2100 images covering seven snail species, then processed through preprocessing and augmentation stages. The model applied was EfficientNet-B0. The results showed that the EfficientNet-B0 model achieved 98.80% training accuracy and 98.33% validation accuracy. Confusion matrix testing showed good performance, with an accuracy of 98% and for the species Oncomelania hupensis lindoensis had a recall of 93%, precision of 100%, F1-score of 97%, and the resulting AUC value of 99.7%. This research successfully developed an efficient identification system, which is expected to help health surveillance personnel in accelerating the identification process of schistosomiasis intermediate hosts.
CNN Algorithm for Herbal Leaf Classification Using MobileNetV2 and ResNet50V2 Pagiu, Harry T.; Kasim, Anita Ahmad; Lapatta, Nouval Trezandy; Pratama, Septiano Anggun; Laila, Rahma
CCIT (Creative Communication and Innovative Technology) Journal Vol 18 No 2 (2025): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i2.3776

Abstract

Indonesia is home to over 30,000 types of herbal plants, with approximately 1,200 species utilized as raw materials for alternative and traditional medicine. Leaves play a crucial role in herbal medicine preparation. However, many people struggle to identify different herbal leaves due to their similar appearances, making classification difficult. Each leaf possesses unique characteristics such as shape, size, midrib, stalk, blade, and type, which can be used for differentiation. To assist in identifying herbal leaves, a classification system based on image recognition is essential. Convolutional Neural Networks (CNN) are deep learning algorithms designed for processing two-dimensional image data. Model performance can be enhanced through transfer learning, with MobileNetV2 and ResNet50V2 being widely used architectures. These pretrained models have been trained to recognize images with high accuracy. This study focuses on classifying herbal plants based on leaf shape using CNN architectures from MobileNetV2 and ResNet50V2. The evaluation results show that the MobileNetV2 architecture, with a 90%:10% data split, achieved an accuracy of 98.51%, precision of 98.92%, recall of 98.51%, and an F1-score of 98.56%. These findings indicate that CNN with transfer learning can effectively classify herbal leaves with high accuracy.
Analysis and Design of Food Price Data Processing Information System Priska, Salsa Dilah; Syahrullah, Syahrullah; Nugraha, Deny Wiria; Lapatta, Nouval Trezandy; Lamasitudju, Chairunnisa Ar
CCIT (Creative Communication and Innovative Technology) Journal Vol 19 No 1 (2026): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v19i1.3904

Abstract

Food prices have an important role in maintaining economic stability and public welfare, as price fluctuations can have a direct impact on purchasing power and inflation. The manual process of recording and reporting food price data at the Department of Agriculture and Food Security of Palu City leads to inefficiencies, data inaccuracies, and difficulties in tracking historical information. These limitations highlight the need for a structured system that can support accurate and efficient data management. This study applies a prototyping method to develop a web-based information system tailored to the needs of the institution. The development process involves continuous interaction between users and developers to ensure the system meets practical requirements. Data were collected through interviews, observations, and documentation. System functionality was tested using black box testing, while usability was assessed using the System Usability Scale (SUS) questionnaire. The results indicate that the system's features, including daily price input, automatic average calculations, report submission, and approval workflows, function correctly. Users are able to interact with the system efficiently, and the SUS results show that the system falls into the acceptable usability category, indicating that it is easy to use. In conclusion, the development of this web-based information system improves the efficiency and accuracy of food price data processing and reporting. It provides a reliable tool for managing information within the department and supports better operational performance.
Evaluating IT Service Capability of Palu BPS Website Using COBIT 5 Framework Ningsih, Alief Surya; Lapatta, Nouval Trezandy; Laila, Rahmah; Kasim, Anita Ahmad; Joefrie, Yuri Yudhaswana; Anshori, Yusuf
CCIT (Creative Communication and Innovative Technology) Journal Vol 19 No 1 (2026): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v19i1.3909

Abstract

This research assesses the IT service capability of the official website of the Palu City Central Bureau of Statistics (BPS) by applying the COBIT 5 framework. The assessment is centered on four key processes from the Deliver, Service, and Support (DSS) as well as Monitor, Evaluate, and Assess (MEA) domains—namely DSS01 (Manage Operations), DSS02 (Manage Service Requests and Incidents), DSS06 (Manage Business Process Controls), and MEA01 (Monitor, Evaluate, and Assess Performance and Conformance). Data were collected through structured interviews, observation sessions with website administrators, and an analysis of supporting documents to determine the current capability levels and compare them with the desired target level of 3. The results show that DSS01 and MEA01 have reached capability level 2, indicating that the processes are defined but not consistently standardized. Meanwhile, DSS02 and DSS06 remain at level 1, indicating reactive operations with limited documentation. The average capability level of 1.5 suggests that there is room for significant improvement in terms of documentation, process formalization, and the use of enabling technologies. Based on these findings, this study recommends targeted improvements to enhance the overall performance and reliability of digital public services, as well as to support better IT governance and e-government practices.
Implementation of ResNet-50-Based Convolutional Neural Network For Mobile Skin Cancer Classification Asriani, Asriani; Lapatta, Nouval Trezandy; Nugraha, Deny Wiria; Amriana, Amriana; Wirdayanti, Wirdayanti
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9696

Abstract

The skin is one of the most important parts of the human body, serving vital functions such as protecting internal organs from injury, shielding against direct bacterial exposure, regulating body temperature, and more. However, the skin is also susceptible to diseases, one of which is skin cancer. Skin cancer can be extremely dangerous if not treated promptly, as it can lead to death. Therefore, early detection is crucial. This study proposes a technology-based solution by classifying skin cancer using a convolutional neural network (CNN) with a ResNet50 architecture implemented into a mobile application via a REST API using Flask. The HAM10000 dataset, consisting of 10,015 skin lesion images across seven classes, was used for model training. Various testing scenarios were conducted to determine the optimal parameter combination. The best results were achieved with an accuracy of 83.84%, precision and recall of 83%, and an F1-score of 83%, using a training data configuration of 70%, dropout of 0.4, and a batch size of 64. The model implemented in this Android application can perform early detection of skin cancer quickly, practically, and easily accessible to the general public, though healthcare professionals must still supervise it. However, although this model can assist users in making early predictions, the prediction results from this model are only a tool for early detection and do not replace clinical diagnosis by professional medical personnel.2) Figure 8 shows the display for taking pictures through the gallery or camera. Users can choose the image they want to upload from the gallery or the camera to be analysed and predicted by the model.
Bahasa Inggris Abdillah Sani, Ilham; Lapatta, Nouval Trezandy; Ngemba, Hajra Rasmita; Fahlevi, Mohammad Fazrin
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4307

Abstract

Implementing a digital-based hazardous work licensing management system at PT Citra Palu Minerals is intended to enhance the efficiency and transparency of the work permit process. The research methodology involves a qualitative approach, Agile methodology system development, and integration with WhatsApp for notifications. The research findings indicate that this system simplifies the submission, approval, and monitoring of work permits in a structured manner, thereby reducing the risk of work accidents. Black box testing demonstrates that the system's performance meets expectations, while the questionnaire results indicate a high level of user satisfaction with an average score of 4.3 out of 5. Implementing this system can serve as a model for enhancing occupational safety and health management in similar industries.
TWITTER (X) SENTIMENT ANALYSIS OF KAMPUS MERDEKA PROGRAM USING SUPPORT VECTOR MACHINE ALGORITHM AND SELECTION FEATURE CHI-SQUARE Sari, Mutiara; Syahrullah, Syahrullah; Lapatta, Nouval Trezandy; Ardiansyah, Rizka
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2037

Abstract

Ministry of Education, Culture, Research and Technology (Kemendikbudristek) has implemented numerous policies aimed at enhancing the quality of education in the country. One of these policies is Kampus Merdeka program. The program includes various initiatives such as Teaching Campus, the Merdeka Student Exchange program, and Internship and Independent Study programs, which have gained significant popularity among students across Indonesia. However, the Kampus Merdeka program has drawn many pros and cons, with some parties supporting the initiative, but also many criticisms related to its implementation, which is considered not optimal in some educational institutions. Social media is where many of these opinions are voiced, one of the most widely used of which is twitter. In light of these circumstances, this study conducted a sentiment analysis of the independent campus program to assess public sentiment towards it. The dataset used in this research consisted of 500 tweets containing the keyword "kampus merdeka" with 250 tweets reflecting positive sentiment and 250 tweets reflecting negative sentiment. The results of the tests carried out obtained the highest increase in results in the 10:90 ratio, namely with an accuracy that increased by 14% from the previous 66% to 80%, precision also increased by 22% from the previous 67% to 89%, recall increased by 16% from the previous 58% to 79%, and the f1-score value which was previously 62% turned into 79% because it also increased by 17%.
Predicting Potential Car Buyers using Logistic Regression Algorithm Lapatta, Nouval Trezandy; Husin, Abdullah
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.4068

Abstract

This research aims to develop a predictive model to identify individuals with a high potential to become car buyers, employing logistic regression algorithm. The primary objective is to support the automotive industry in devising more efficient and focused marketing strategies. The choice of logistic regression is based on its superiority in handling categorical dependent variables and its practicality in result interpretation. The data processed in this study derive from demographic information, consumption habits, brand preferences, and various other factors that influence car buying decisions. The main data source is the outcome of online surveys participated in by individuals predicted to have the potential to buy a car within the next 12 months. The analysis results indicate that factors such as income, age, previous vehicle ownership status, gender and marriage status play significant roles in predicting the likelihood of someone becoming a car buyer. The developed model achieved an accuracy and precision of 95%, proving its significant capability in identifying potential car buyers with a high success rate. These findings provide valuable insights for the automotive industry in formulating more targeted and efficient marketing strategies, as well as contributing to the academic literature on the application of logistic regression in consumer behavior prediction.
Usability and User Experience Evaluation on Extracurricular Website (SINEMA) Implementation using SUS and UEQ Methods Wirdayanti; AKBAR, MUHAMMAD; Sabarudin Saputra; Deni Luvi Jayanto; Sri Khaerawati Nur; Nouval Trezandy; Bakri
Information Technology International Journal Vol. 3 No. 2 (2025): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v3i2.57

Abstract

The rapid integration of web-based platforms in higher education highlights the importance of usability and user experience in supporting students’ extracurricular activities. This study evaluates the usability and user experience of the Student Extracurricular Information System (SINEMA) developed at Tadulako University. A total of 99 respondents participated, selected from a population of 5,581 active users through Slovin’s formula. Two standardized instruments were applied: the System Usability Scale (SUS) to capture global usability perceptions and the User Experience Questionnaire (UEQ) to assess six dimensions of user experience. The SUS results indicate a mean score of 76.06, which falls within Grade B and the “Good” category, exceeding the global benchmark. This suggests that the system is generally usable, although certain respondents reported minor challenges requiring further improvement. The UEQ results show that Perspicuity (1.94), Dependability (1.94), and Stimulation (1.93) achieved the “Excellent” category, reflecting clarity, reliability, and engagement. Meanwhile, Attractiveness (1.65), Efficiency (1.58), and Novelty (1.72) were rated “Good,” highlighting positive perceptions but also opportunities for optimization. Overall, the findings demonstrate that SINEMA effectively supports extracurricular management with satisfactory usability and strong user experience. The study contributes novelty by integrating SUS and UEQ for comprehensive evaluation within a higher education extracurricular context. Recommendations include enhancing efficiency and novelty to elevate user satisfaction and system adoption.
Co-Authors ., Rezki Abdillah Sani, Ilham Abdul Mahatir Najar Abdullah Abdullah Adhira Putri, Dhivanny Agung Stiven Cahyati Angely Ain, Moch. Zukhruf Aldiza Intan Randani Amriana Amriana Amriana Amriana Andhyka, Andhyka Andi Hendra Andi Hendra Angraeni, Dwi Shinta Anita Ahmad Kasim Arsita, Tiara Juli Asriani Asriani, Asriani Aulia Rakhman Ayu Anita Ayu Hernita Ayu Hernita Bakri Chairunnisa Lamasitudju Chandra, Ferri Rama Delia, Fenita Deni Luvi Jayanto Deny Wiria Nugraha Dessy Santi Djohari, Riyandi Dwitama Dwi Shinta Angreni Dwi Shinta Angreni Fahlevi, Mohammad Fazrin Fajar, Moh Fajriyah, Nurul Faldiansyah, Faldiansyah Firzatullah, Raden Muhamad Hajra Rasmita Ngemba Hamid, Odai Amer Hanama, Ikhsan Wahyudin Harlin Feby Karnita Sumbaluwu Ihalauw, Sahron Angelina Ihwan, Abib Raifmuaffah Ipham Ahmad Fahrezy Farid Iskandar skandar Kartika, Rina Laila, Rahma Lamadjido, Moh. Raihan Dirga Putra Lamasitudju, Chairunnisa Laura Paige Pasha Mandra Meilani Ilman Mohamad Irfan, Mohamad Mohammad Wandy Mohammad Yazdi Pusadan Muh. Ashari Rasyid Muhammad Akbar Muhammad Akbar Muhammad Akbar Muhammad Rifaldi Dwimanhendra Muhammad Syahputra Maulana Muhammad Zaidan Murtafiatun Darojah Mutiara Sari Ngemba, Hajra Nikmah Utami Dewi Ningsih, Alief Surya Noel Marcell Jonathan Wongkar Noviantika, Noviantika Nurhikmah Supardi Nursiana Zasqia, Andi Nirina Pagiu, Harry T. Paloloang, Muhammad Fadhil Akmal B. Priska, Salsa Dilah Putra, Adhitya Pramana Qofifa, Sitti Nurlaili Rahma Laila Rahmah Laila Rahmah Laila Rasmita Ngemba, Hajra Rasmita, Hajra Rinianty Rinianty Rinianty, Rinianty Rizka Ardiansyah Rizka Ardiansyah Rizka Ardiansyah Rizky, Moh Taufiq Ryfial Azhar Ryfial Azhar Saada, Rahmadian A. Sabaruddin Saputra Sabarudin Saputra Sahril Sahril Septiano Anggun Pratama Setiawan, Dita Widayanti Siti Rahmawati Sri Khaerawati Nur Sukirman Sukirman Syahrullah Syahrullah Syahrullah Syaiful Hendra Syaiful Hendra Tri Krama Wirdayanti Wirdayanti Wirdayanti Wongkar, Noel Marcell Jonathan Yanti, Wirda Yuri Yudhaswana Joefrie Yusuf Anshori Zulkifli Zulkifli