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All Journal Jurnal Informasi dan Teknologi Jurnal Kolaboratif Sains International Journal Software Engineering and Computer Science (IJSECS) Jurnal Sistim Informasi dan Teknologi
Muhamad Ziaul Haq
Universitas Muhammadiyah Palu
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Other

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Prediksi Indeks Prestasi Komulatif Mahasiswa berdasarkan Nem dengan Menggunakan Algoritma Neural Network Berbasis Particle Swarm Optimization : Prediction of Student Comulative Achievement Index Based on NEM Using Particle Swarm Optimization Based Neural Network Algorithm Muhamad Ziaul Haq; Nursalim
Jurnal Kolaboratif Sains Vol. 6 No. 2: FEBRUARI 2023
Publisher : Universitas Muhammadiyah Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/jks.v6i2.3303

Abstract

Proses Penerimaan Mahasiswa Baru (PMB) pada berbagai perguruan tinggi di Indonesia baik itu perguruan tinggi negeri ataupun swasta melakukan seleksi terhadap calon mahasiswanya dengan melihat pada Nilai Ebtanas Murni (NEM). Guna menganalisis hubungan antara nilai NEM calon mahasiswa dengan prestasi akademik yang dicapai di STMIK Adhi Guna (dalam hal ini digunakan indeks prestasi kumulatif (IPK) dengan analisis korelasi dan regresi linier ganda. Variabel penelitian yang digunakan adalah hasil IPK kelulusan mahasiswa sebagai variabel dependen (terikat), dan nilai mata pelajaran Bahasa Indonesia, Bahasa Inggris, dan Matematika sebagai variabel independent (bebas). Penelitian ini menggunakan 2 algoritma yang berbeda yaitu Neural Network (NN) dan Particle Swarm Optimization (PSO) untuk membandingkan hasil dan mencari tingkat akurasi terbaik diantara kedua algoritma tersebut. Dari penelitian yang telah dilakukan dapat disimpulkan bahwa Neural Network berbasis Particle Swarm Optimization adalah algoritma yang paling baik dibandingkan dengan Neural Network untuk mengukur tingkat korelasi antara NEM dan Indeks Prestasi Kumulatif (IPK) mahasiswa. Neural Network pada penelitian ini menghasilkan akurasi terkecil 0,214 dengan Time = 4 s. Neural Network berbasis PSO menghasilkan akurasi terkecil 0,132.
The Implementation of Simple Additive Weighting Method for Designing A Web-Based Waste Management Saving Transaction System Nursalim; Muhamad Ziaul Haq; Nalis Hendrawan; Roy Mubarak; I Putu Dody Suarnatha
Jurnal Sistim Informasi dan Teknologi 2023, Vol. 5, No. 2
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jsisfotek.v5i2.244

Abstract

The Garbage Bank is an organization initiated for all students to support the management of inorganic waste into something of value so as to create additional income. The increasing number of customers has caused the treasurer to be overwhelmed in ranking customer rankings and to not be on target in determining the best customer with the same amount of waste. In addition, there is no system security in handling the transaction process, so unwanted access can occur. The purpose of this research is to develop a savings transaction system for waste management so that it becomes a green Campus. The decision-making method uses Simple Additive Weighting. The system development methodology used is Rapid Application Development (RAD). The tools used in system design are the Unified Modeling Language. The implementation of this system uses the PHP programming language with the Laravel and MySQL frameworks for database processing. The resulting system can simplify and speed up the process of recording and managing waste bank data.
The Application of Information Technology Architectural Design Using TOGAF Architecture Framework in Restaurant Service Systems Sri Wahyuningsih, Suluh; Ziaul Haq, Muhammad; Hamid, Helson; Hady, Sultan; Hendrawan, Nalis
Jurnal Informasi dan Teknologi 2023, Vol. 5, No. 4
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.v5i4.429

Abstract

This research aims to see how TOGAF ADM is applied to modeling information technology architecture in restaurants. In this research, the author used the TOGAF Architecture Development Method (ADM). In TOGAF ADM, there is a definition of architecture and its understanding, which is in the preliminary phase (the preparatory phase). In this modeling, it starts from zero, so a detailed architectural process is needed. This is needed to simplify the subsequent architectural development process. Detailed architectural processes can be obtained using the framework. In TOGAF ADM, there are stages that have been arranged in such a way that the details of the architecture can be seen in them. The modeling that the author will compile also requires support for architectural evolution. This is needed because, initially, the restaurant did not have technological architecture. In Phase F Migration Planning, the framework provides support for technology architecture evolution. Based on the research steps, there are 8 structured stages plus a preliminary stage. However, in this research, the author will only discuss up to stage F, namely migration planning. From the research conducted by the author, a model of information technology architecture for restaurants was obtained, which was implemented using TOGAF ADM. The information technology architecture model includes service processes, payment processes, and monitoring processes.
Application of the K-Nearest Neighbor Algorithm Method to Analyze Netizen Responses and Reactions Toward the Relocation of Capital City at social media Ziaul Haq, Muhammad; Sri Wahyuningsih, Suluh; Nursalim; Nuryanto, Uli Wildan; Rachman, Andy
Jurnal Informasi dan Teknologi 2023, Vol. 5, No. 4
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.v5i4.458

Abstract

Sentiment analysis is a form of natural language processing that uses word analysis to ascertain people's thoughts, feelings, and views on a certain topic. In this study, word processing refers to the procedure used to categorize written texts into positive and negative emotion categories. Using data crawling techniques, information on public comments on the relocation of Indonesia's capital was gathered from Twitter social media. Keywords related to the move included "new capital," "moving capital," and "moving capital with 10,000 comments." The author of this work classified test data and training data using a lexical approach using the K-Nearest Neighbor (K-NN) method. The purpose of this study is to evaluate the K-NN algorithm's accuracy, error rate, precision, f-measure, and recall. In order to identify the ideal parameters, tests were also conducted on calculating the k value in the K-Nearest Neighbor (K-NN) method. Testing the K-Nearest Neighbor (K-NN) method yielded the greatest accuracy level of 60% with a k value of 9, concluding with the initial data collection. The K-Nearest Neighbor (K-NN) technique was evaluated in the second data collection, and with a k value of 5, it had the best accuracy level of 70%. Future scholars might create texts in languages other than Indonesian and categorize those that include visuals in them. Next, add more dictionaries to the collection and extract features from bigrams, trigrams, quadgrams, and other combinations. You may then employ several algorithmic techniques in the accuracy calculation feature.
Deep Learning Based Augmented Reality for 3D Object Recognition Muhamad Ziaul Haq; Nursalim Nursalim
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5431

Abstract

Augmented Reality (AR) technology is being widely adopted in various fields such as education, entertainment, and creativity. However, there are still some challenges to be overcome in recognizing and rendering three-dimensional (3D) objects accurately and in real-time. We implemented an AR system that utilizes deep learning techniques to recognize 3D objects with improved accuracy levels. Our approach involved training a Convolutional Neural Network (CNN) model using 3D object datasets captured from different viewpoints. The development included designing the network architecture, training the model, evaluating its accuracy, and integrating it into an AR platform based on Unity 3D and Vuforia SDK. The results indicated that the system could achieve recognition of the 3D objects with an average accuracy of 93.7%, precision of 92.4%, and recall of 91.8%, all while keeping response times below 0.8 seconds. Objects with complex geometries like cars and chairs had recognition rates above 94%, while those with similar textures had lower accuracy because of detailed surface complexities. It allows stable interactive visualization of objects in augmented reality even under different lighting conditions and camera angles. Combining deep learning with AR improves the quality of object recognition and provides a more realistic interactive experience. This paper discusses the advances made in AR technology toward better adaptability and efficiency, which can be applied to interactive education, industrial simulation, architecture, and medical fields.
Co-Authors Andy Rachman Hady, Sultan Hamid, Helson Hendrawan, Nalis I Putu Dody Suarnatha Nalis Hendrawan Nursalim Nursalim Nursalim Roy Mubarak Sri Wahyuningsih, Suluh Uli Wildan Nuryanto