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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sains dan Informatika ILKOM Jurnal Ilmiah Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali JURNAL IT: Media Infromasi STMIK Handayani Makassar E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Fokus Elektroda: Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Ilmu Komputer untuk Masyarakat Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Technology Research in Information System and Engineering Jurnal Fokus Elektroda (Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali)
Zuhriyah, Sitti
Universitas Handayani Makassar
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Mathematics Other

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Implementasi Metode Naïve Bayes untuk Pemilihan Jenis Tanaman Reboisasi berdasarkan Kondisi Lahan: (Studi Kasus Reboisasi Kawasan Hutan Sulawesi Selatan) Suwatri Jura; Billy Eden William Asrul; Abdul Rochman; Sitti Zuhriyah
Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Vol. 8 No. 1 (2023): Jurnal Fokus Elektroda Vol 8 No 1 2023
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Halu Oleo

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Abstract

Tanaman untuk reboisasi ditanam dan dipelihara dengan tujuan memperbaiki hutan atau non hutan yang mengalami kerusakan pada suatu area lahan, dimana lahan dapat dimanfaatkan oleh manusia untuk melakukan kegiatan menjaga ekosistem hutan. Untuk menentukan jenis tanaman reboisasi yang sesuai dengan kondisi lahan, terdapat beberapa kesulitan yang dihadapi. Penelitian ini bertujuan mengimplementasikan metode Naive Bayes untuk menyesuaikan manfaat tanaman dengan kondisi lahan yang rusak, yang meliputi topografi, iklim, dan jenis tanah. Proses pada penelitian ini dilakukan dari tahap data cleaning, data integration, dan Classification. Pengujian dilakukan dengan 2 Tahap, pengujian dilakukan untuk menentukan akurasi dari Model Naive Bayes terhadap data dan hasil luaran Aplikasi. Data diuji dengan 10-fold validation. Berdasarkan Hasil skenario kedua tahap pengujian yang dilakukan pada penelitian ini, diperoleh tingkat akurasi skenario pertama sebesar 96% dan akurasi pengujian tahap kedua sebesar 83.53%, rata-rata akurasi dari kedua tahap pengujian didapatkan hasil sebesar 89,76%.      
Implementasi Algoritma Hue Saturation Value (HSV) Pada Penentuan Kualitas Beras Berbasis Android Yuliana Aprilia Anwar; Billy Eden William Asrul; Sitti Zuhriyah
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Rice is the staple food of most Indonesian people. Rice contributes more than 22% of global energy intake. Indonesia, especially South Sulawesi is a major rice producer where rice production is around 92% of total world production 1.2 For nations in Asia. This research aims to implement the Hue Saturation Value (HSV) algorithm for extracting color features in Android-based rice quality determination. In this research there were 600 images of rice which were divided into 80% training data and 20% testing data. Training samples are used for color feature extraction by applying Red, Green and Blue (RGB) features to the Hue Saturation Value (HSV) method. Next, image identification is carried out to determine the quality of the rice using color features. From the test results using the confusion matrix at a value of K=7, precision was 82%, recall was 90%, F-1 score was 86%, and accuracy was 85%
Strategi Hukum Preventif dalam Meningkatkan Perlindungan Anak di Era Digital Muh. Fachrur Razy Mahka; Fatri sagita; Najirah Umar; Sitti Zuhriyah; Nur Lilis Sukanda
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Technology advancement is inevitable in this life. The role of parents in supervising and directing the use of children's technology is very important. The purpose of this study is to determine preventive legal strategies to improve child protection in the digital age and the role of parents in Law No. 35 of 2014 on Child Protection in the digital age. The type of research used is qualitative research. The author uses a normative-juridical and sociological approach. In this study, the key informants are parents of children in Paccinongang Village, Somba Opu District, Gowa Regency. Data collection will be carried out using four methods, namely interviews, observations, and library research. The results of this study found that there are several preventive legal strategies that can be used, including the formulation and updating of regulations and digital literacy education in schools, as well as the role of parents according to Law No. 35 of 2014 on Child Protection, which is very important in maintaining and protecting the rights of children in Indonesia. This law brings relevant basic principles that can be applied in the context of child protection in the digital world. The researcher can conclude that preventive legal strategies, such as the development and updating of relevant regulations, are important steps in identifying potential risks and threats to children in the digital age. Digital literacy education in schools is a step in preparing children to face the increasingly complex digital world. The role of parents in child protection in the digital age is also essential. They must ensure the safety of children in their use of technology, protect their privacy, and provide guidance on online ethics and good behavior.
Machine Learning and Internet of Things (IoT): A Bibliometric Analysis of Publications Between 2012 and 2022 Gani, Hamdan; Damayanti, Annisa Dwi; Nurani, Nurani; Zuhriyah, Sitti; Jabir, St. Nurhayati; Gani, Helmy; Zhipeng, Feng; Rejeki, Aisyah Sri
ILKOM Jurnal Ilmiah Vol 16, No 1 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i1.1700.27-37

Abstract

The implementation between machine learning and the Internet of Things (IoT) has been scientifically investigated in many studies. However, not many bibliometric studies categorize the output in this area. By keeping an eye on the publications posted on the Web of Science (WoS) platform, this study aims to give a bibliometric analysis of research on Machine Learning and IoT, identifying the state of the art, trends, and other indicators. 6.170 different articles made up the sample. The VOS viewer software was used to process the data and graphically display the results. The study examined the concurrent occurrence of publications by year, keyword trends, co-citations, bibliographic coupling, and analysis of co-authorship, countries, and institutions. several prolific authors are discovered. However, the body of literature on machine learning and IoT issues is expanding quickly; only five papers accounted for more than 2193 citations. Then, 40.34 percent of the articles from the 694 sources reviewed were published as the most important paper. At the same time, the USA is the top nation for research on this subject area. In addition to identifying gaps and promising areas for future research, this study offers insight into the current state of the art and the field of machine learning and IoT.
Sistem Pendukung Keputusan untuk Penentuan Klasifikasi Status Hotel Asrul, Billy Eden William; Zuhriyah, Sitti; Anatasya, A. Edeth Fuari
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The Hotel industry is a key component of the tourism sector that is required to obtain business certification. This certification determines the star classification of a hotel. The determination of 1, 2, 3, 4, and 5-star classifications for hotels, particularly in the city of Makassar, involves the Tourism Business Certification Agency (LSUP) as the auditing body, the Makassar City Tourism Office as the regulatory body responsible for supervision and guidance, and the industry as the auditee. The conventional and manual processes for determining star ratings reduce the effectiveness and efficiency of audit implementation, as well as hinder the supervision of business certification by the Makassar City government.
Augmented reality application on the tourism orientation sign digital system at the 'Bawah Langit' museum Zuhriyah, Sitti; William Asrul, Billy Eden; Jura, Suwatri
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.857.216-225

Abstract

One of the most visited tourism icons in Makassar is the Losari Beach Pavilion which provides many choices ranging from beautiful sunsets, typical banana epe cuisine, floating mosques, and rows of statues of heroes and icons of Makassar City which are spread over four platforms, namely Makassar Pavilion, Bugis Pavilion, Mandar and Toraja which is commonly called the Museum Under the Sky. In supporting the attraction, it is necessary to be equipped with an accurate explanation of the statues in the attraction. In the new normal that limits interaction with other people, digital and virtual explanations will be one solution to provide attractive and up-to-date information. This study aims to create a Digital Tourism Orientation Sign System, which is an information system that provides accurate explanations and descriptions of the hero statues in the Underground Sky Museum. With this application, the Losari Pavilion is one of the futuristic tourist attractions with local wisdom. This application has been tested using Alpha testing which includes distance testing with test results a minimum distance of 10 cm and a maximum distance of 1 m marker will be detected; light testing with object test results will appear from a room brightness level greater than 10 Lux.
Identification of Papua Cenderawasih Batik Motifs using Local Binary Pattern and K-Nearest Neighbor Ariani, Dian Dwi; Zuhriyah, Sitti; Puspaningrum, Eva Yulia; Pallawabonang, Mahabintang
Sistemasi: Jurnal Sistem Informasi Vol 14, No 2 (2025): 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.v14i2.5008

Abstract

Papua Island has natural and cultural richness wich is reflected in its batik motifs, such as the Cenderawasih and Tifa motifs. Although batik recognition technology has developed, systems capable of automatically identifying Papua batik motifs are still limited. This research aims to develop a texture recognition system using the Local Binary Pattern (LBP) feature extraction method and K-Nearest Neighbor (KNN) classification. The Cenderawasih motif dataset consists of 115 images, and the Tifa motif dataset consists of 120 images with an 80:20 composition for training and testing data. We tested the KNN model with various k values and found that k = 7 yielded the best results, with accuracy of 97.16%, precision of 97.10%, and F1-score of 97.10%. The developed GUI interface facilitates users in identifying batik motifs, providing prediction results, and texture visualization. The results of this study show that image processing technology could help protect Papuan batik. Future research could improve model accuracy by utilizing larger data sets and classification algorithms to make the models more accurate.
Implementasi Algoritma Learning Vector Quantization untuk Deteksi Dini Penyakit Mata Mansur, Mansur; Umar, Najirah; Zuhriyah, Sitti
Jurnal Sains dan Informatika Vol. 11 No. 1 (2025): Jurnal Sains dan Informatika
Publisher : Teknik Informatika, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v11i1.907

Abstract

The eye is one of the senses of human vision that is very important in human life. The lack of online eye health consultation services is often ignored by the public because it considers eye diseases not to be dangerous diseases and have no impact on everyday life. On this issue, then developed an eye disease detection system using the Learning Vector Quantization method. (LVQ). This method is capable of giving a classification of patterns that would represent a particular class. In this study, there are 25 symptoms and 10 eye diseases that will be processed in training and testing with the data being divided into training and test data. The LVQ method will perform several steps to obtain the final weight. Using the LVQ method, the parameter values obtained include Learning rate 0.1, 0.2, Iteration 1 and 2. In the accuracy test of this system, the average accuracy result received with the training data 90 Iterations 2 Learning rates 0.1, testing of the test data 19 yielded accuration of 100% and Iterating 2 Learning rate 0,2 testing of testing data 19 was accurate of 100%. which indicates that the system can function properly. So the LVQ method can be applied to the classification of eye diseases.
Integration of YOLOv5 Algorithm and OpenCV in Innovative Smart Parking Management Approach Hidayah, Akmal Hidayah; Sitti Zuhriyah; Billy Eden William Asrul; Yuyun, Yuyun; Esa Prakasa
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 3 (2024): June 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i3.5728

Abstract

The problem of automatic parking lot identification and vehicle detection in open areas is becoming increasingly important due to the increase in the number of vehicles in Indonesia, particularly in big cities, resulting in difficulties in finding parking spaces during peak hours. In this condition, drivers often have to compete for parking spaces. This research aims to develop a smart parking system that integrates YOLOv5 and OpenCV algorithms. This approach thoroughly combines both algorithms to identify parking spaces and detect vehicles in real time in various parking scenarios. It is carried out in an open area with reference to parking conditions at the BRIN Bandung office. This study collected data from three different parking lot conditions, namely empty, partially occupied, and full. In each condition, the system successfully detected the parking lots and vehicles accurately. The novel contribution of this research is the development of a smart parking system that uses an integrated approach, providing an effective solution to the challenges of parking lot availability and vehicle detection. Using the advantages of both algorithms, we successfully created a system that can identify parking spaces and detect vehicles accurately and efficiently under various parking circumstances. Therefore, this research makes a significant contribution to the development of smart and adaptive parking management technology.
Classification of Toraja Wood Carving Motif Images Using Convolutional Neural Network (CNN) Nurilmiyanti Wardhani; Asrul, Billy Eden William; Antonius Riman Tampang; Sitti Zuhriyah; Abdul Latief Arda
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5897

Abstract

Wood carving is a cultural heritage with deep meaning and significance for the Toraja ethnic group's culture. By understanding the meaning of each Toraja carving, both tourists and the local community can gain knowledge about Toraja culture, thereby preserving and maintaining the culture amidst modern developments. Image processing approaches, particularly the development of Convolutional Neural Networks (CNN), offer a solution for extracting information from the diverse and intricate patterns of Toraja wood carvings. This study is highly significant as it implements a deep learning model using the CNN algorithm optimized with the ResNet50 architecture. The methodology in this study involves adjusting the batch size during the model training phase and applying weak-to-strong pixel transformation during the double threshold hysteresis phase in the Canny Feature Extraction process on the edges of Toraja carving images, resulting in ResNet50 architecture accurately recognizing the patterns of Toraja wood carvings. The results demonstrate significant improvements in the performance of the ResNet50 architecture with the preprocessed dataset. average precision, recall, precision, and F1-Score improvements in each Toraja carving class. For the Pa' Lulun Pao class, it was found that the precision and recall values were 0.94, and the F1-Score was 0.94. The Pa’ Somba class also showed good results, with a precision value of 0.9697, a recall of 0.96, and an F1-Score of 0.9648. The Pa’ Tangke Lumu class showed even better results, with a precision value of 0.9898, a recall of 0.97, and an F1-Score of 0.9798. The Pa’ Tumuru class also demonstrated good performance, with a precision value of 0.9327, a recall of 0.97, and an F1-Score of 0.9500. This study not only underscores the effectiveness of processing in enhancing CNN capabilities but also opens opportunities for further research in applying these methods to various image types and exploring different CNN architectures.
Co-Authors Abdul Latief Arda Abdul Rochman Anatasya, A. Edeth Fuari Antonius Riman Tampang Ariani, Dian Dwi Billy Eden William Asrul Damayanti, Annisa Dwi Esa Prakasa Etika Vidyarini Eva Yulia Puspaningrum Fatri Sagita Feng Zhipeng Fuari Anatasya, A. Edeth Gani, Helmy Gani, Helmy Hamdan Gani Hidayah, Akmal Hidayah Jabir, St. Nurhayati Jura, Suwatri Luh Putu Ratna Sundari Makmur, Anugrah Mansur Mansur Muh. Fachrur Razy Mahka Najirah Umar Namruddin, Respaty Nur Lilis Sukanda Nur, M. Adnan Nurani Nurani Pallawabonang, Mahabintang Pujianti Wahyuningsih Rejeki, Aisyah Sri Wahyuningsih, Pujianti Wardhani, Nurilmiyanti Yuliana Aprilia Anwar Yuyun, Yuyun Zhipeng, Feng