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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Bulletin of Computer Science Research Jurnal Ilmu Komputer Jurnal Teknologi Informasi Indonesia
Lamusu, Rizal
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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ANALYSIS OF COVID-19 GROWTH TRENDS THROUGH DATA MINING APPROACH AS DECISION SUPPORT Abas, Mohamad Ilyas; Ibrahim, Irawan; Syahrial, Syahrial; Lamusu, Rizal; Baderan, Umar Sako; Kango, Riklan
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.11861

Abstract

This study aims to analyze the growth trend of covid-19 using prediction algorithms in data mining for covid-19 data throughout Indonesia. This can be used as a decision support to analyze several government policies towards regulatory intervention so far. The method used is the best prediction method in time series data, including Neural Network, SVM, Linear Regression, K-Neirest Neighborn and optimizes it with optimization algorithms. This research is focused on the application of these applications. It is hoped that this research will produce an analysis of the growth trend of Covid cases every day, in addition to its contribution so that it can assist the government in determining the best policy direction and also as an education to the public. in addition, the research will contribute to science in the field of predictive analysis by finding the best RMSE formulation. The results of this study show that Neural Network-Particle Swarm Optimization has the smallest Roort Mean Square Error which is 265,326, and the two Neural Network Genetic Algorithm are 266.801, Neural Network Forward Selection is 275,372 and Neural Network without optimization has the largest RMSE which is 297.204. These results can be used as a reference for the use of similar algorithms in time series data, both Covid-19 data and other data.
KONSTRUKSI ALGORITMA PEWARNAAN TITIK PELANGI PADA GRAF POHON Pranata, Widya Eka; Abas, Mohamad Ilyas; Ibrahim, Irawan; Lamusu, Rizal; Syahrial, Syahrial
Jurnal Ilmu Komputer (JUIK) Vol 5, No 1 (2025): February 2025
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v5i1.3839

Abstract

This study constructs an algorithm for rainbow vertex coloring in tree graphs. A graph G is defined as a set pair G=(V,E), where V is the vertex set and E is the edge set. Rainbow dot coloring aims to assign a color to each vertex of a tree graph T, such that each path in the tree has vertices with unique colors. The rainbow dot coloring algorithm developed in this study is implemented in a programming language, and its performance is evaluated through simulations on various types of tree graphs. The results show that this algorithm can effectively color tree graphs with good optimality. This research contributes to graph coloring theory and its potential to be applied to computational problems involving tree graphs with complex structures.
Digital Library Universitas Muhammadiyah Gorontalo Antupetu, Rastin; Abas, Mohamad Ilyas; Lasarudin, Alter; Lamusu, Rizal
Jurnal Ilmu Komputer (JUIK) Vol 4, No 1 (2024): FEBRUARY 2024
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v4i1.2797

Abstract

This research was conducted at the Library of Universitas Muhammadiyah Gorontalo. The aim is to design a web-based library information system with a digital concept (Digital Library). The design of the Digital Library information system of Universitas Muhammadiyah Gorontalo, with a system design model using the prototyping model, provides a forum for students and lecturers, especially those on the campus of Universitas Muhammadiyah Gorontalo to publish research results in the form of theses, journals, and theses into this digital library system. With this digital library it will facilitate access for prospective graduates, both the academic community and academics outside the campus, who will take literature as a reference for the final report or thesis. Provide convenience in disseminating useful information or knowledge and help students conduct research.
REPOSITORY PERPUSTAKAAN BERBASIS ANDROID DI UNIVERSITAS MUHAMMADIYAH GORONTALO Lamusu, Rizal; Lasaruddin, Alter; Ibrahim, Irawan; Lestari, Gita
Jurnal Ilmu Komputer (JUIK) Vol 3, No 1 (2023): Februari 2023
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v3i1.2000

Abstract

This research was conducted with the aim of developing the Repository application, initially the system was more directed to journal searches, did not provide reading access rights, no journal download feature and the information was still incomplete so that it would be developed using Android-based technology. Through this research, it is hoped that the journal search process wil be much more this application was developed using the SDLC system development method or Software Development Life Cycle which has several stage, namely Planning, Analysis, Design and Implementation. By using system testing Black-box type Boundry Value Analysis/Limit Testing. The result of this research are in the form of an Android-based library repository application software.
Repositori Berbasis Web Fakultas Sains Dan Ilmu Komputer Universitas Muhammadiyah Gorontalo Lamusu, Rizal; Maku, Rubiyanto; Ibrahim, Irawan; Gobel, Alvian Van
Jurnal Ilmu Komputer (JUIK) Vol 4, No 2 (2024): JUNE 2024
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v4i2.3278

Abstract

The Faculty of Science and Computer Science, Muhammadiyah University of Gorontalo is a higher education entity that has various needs in managing and utilizing data, information and important documents. This research aims to design a web-based repository for the Faculty of Science and Computer Science, Muhammadiyah University of Gorontalo and implement a web-based repository at the Faculty of Science and Computer Science, Muhammadoyah University of Gorontalo. This research was developed using a prototype model, PHP programming language, Laravel framework, MySQL and system design tools using Unified Modeling Language. The results of research using a prototype system development model show that the Web-Based Repository System, Faculty of Science and Computer Science, Muhammadiyah University, Gorontalo has reached the maturity level. which allows its effective use. With this system, it is hoped that employees of the Head of Administration and his staff at the Faculty of Science and Computer Science, Muhammadiyah University of Gorontalo can implement a web-based repository that is effective, efficient, and in accordance with academic and administrative
IDENTIFIKASI KUALITAS DAN JENIS BUAH CABAI MENGGUNAKAN ALGORITMA DEEP LEARNING Supu, Tahir; Syahrial, Syahrial; Lamusu, Rizal; Pranata, Widya Eka
Jurnal Ilmu Komputer (JUIK) Vol 5, No 3 (2025): OCTOBER 2025
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v5i3.4593

Abstract

Abstrak Penelitian ini memuat dua rumusan masalah (1) Bagaimana mengindentifikasi kualitas dan jenis buah cabai berdasarkan Algoritma Deep Learning? (2) Bagaimana meningkatkan Akurasi mengenali kualitas dan jenis buah cabai berdasarkan Algoritma Deep Learning? Tujuan penelitian ini adalah (1) Mengidentifikasi kualitas dan jenis buah cabai secara otomatis menggunakan Algoritma deep learning. (2) Meningkatkan sistem Akurasi mengenali kualitas dan jenis buah cabai dengan mengoptimalkan penerapan Algoritma deep learning. Deep Learning adalah arsitektur jaringan saraf berlapis untuk memproses dan menganalisis data dalam skala besar secara efisien guna menyelesaikan berbagai permasalahan kompleks. Deep Learning merupakan cabang dari Machine Learning yang algoritmanya dirancang mengadopsi arsitektur jaringan saraf biologi manusia, yang dikenal sebagai Artificial Neural Networks. Salah satu, arsitektur Deep Learning yang paling umum digunakan analisis dalam gambar adalah Convolutional Neural Network (CNN). Pada penelitian ini, sampel data yang diperoleh berjumlah 1500 data gambar yang dibagi 4 kelas , yaitu : Cabai keriting segar, Cabai keriting tidak segar, Cabai rawit segar dan Cabai rawit tidak segar. Setalah itu akan di bagi menjadi dua data yaitu train dan validasi Gambar grafik di atas menunjukkan hubungan antara rasio data latih-uji dengan model Akurasi pada ukuran citra 400x400 piksel. Terlihat bahwa model Akurasi cenderung meningkat seiring proporsi data latih, dengan puncak Akurasi terjadi pada 80:20, mencapai sekitar 98% 1. Model CNN CabaiNet yang dikembangkan dalam penelitian ini mampu mengklasifikasikan jenis dan kualitas buah cabai ke dalam empat kelas (cabai keriting segar, cabai keriting tidak segar, cabai rawit segar, dan cabai rawit tidak segar) dengan tingkat Akurasi tinggi, mencapai 99% pada terbaik. Hal ini menjawab rumusan masalah pertama terkait bagaimana identifikasi kualitas dan jenis buah cabai dengan Algoritma deep learning, yaitu dengan memanfaatkan arsitektur CNN khusus yang dirancang untuk mengekstraksi fitur visual dari citra buah cabai1. Model CNN CabaiNet yang dikembangkan dalam penelitian ini mampu mengklasifikasikan jenis dan kualitas buah cabai ke dalam empat kelas (cabai keriting segar, cabai keriting tidak segar, cabai rawit segar, dan cabai rawit tidak segar) dengan tingkat Akurasi tinggi, mencapai 99% pada konfigurasi terbaik. Hal ini menjawab rumusan masalah pertama terkait bagaimana identifikasi kualitas dan jenis buah cabai dengan Algoritma deep learning, yaitu dengan memanfaatkan arsitektur CNN khusus yang dirancang untuk mengekstraksi fitur visual dari citra buah cabai.
Penerapan Teknologi Cloud Computing Untuk Aplikasi Repository Data Di Universitas Muhammadiyah Gorontalo Wati, Nursetia; Lamusu, Rizal
Jurnal Teknologi Informasi Indonesia (JTII) Vol 4 No 2 (2019): Jurnal Teknologi Informasi Indonesia (November)
Publisher : JURNAL TEKNIK INFORMATIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30869/jtii.v4i2.404

Abstract

Current technological developments have supported all needs and requests for information for each individual through the creation of information presentation media, which is used to convey information as desired. In utilizing the technological development itself, researchers intend to apply Cloud Computing technology for data repositories at University of Muhammadiyah Gorontalo.Repository of this data is expected to help the performance of Academic and Supporting Staff and Students in the University of Muhammadiyah Gorontalo. The aim of this research is to be able to apply Cloud Computing technology for database repository applications so that it can help Lecturers and Academic Support Staff and Students for administrative and other purposes that can be stored in a database or file for later distribution using a computer network. This study conducted a stage that began by surveying whether the Muhammadiyah University of Gorontalo had applied this Cloud Computing technology to this data repository, then collected the data needed to create a Repository system. The output of this system was a Repository system containing data from Academic and Academic Support Staff and students at Muhammadiyah University, Gorontalo.
Pembentukan Pola Desain Motif Karawo Gorontalo Menggunakan K-Means Color Quantization dan Structured Forest Edge Detecion Syahrial, Syahrial; Lamusu, Rizal
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 3: Juni 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021834491

Abstract

Sulaman Karawo merupakan kerajinan tangan berupa sulaman khas dari daerah Gorontalo. Motif sulaman diterapkan secara detail berdasarkan suatu pola desain tertentu. Pola desain digambarkan pada kertas dengan berbagai panduannya. Gambar yang diterapkan pada pola memiliki resolusi sangat rendah dan harus mempertahankan bentuknya. Penelitian ini mengembangkan metode pembentukan pola desain motif Karawo dari citra digital. Proses dilakukan dengan pengolahan awal menggunakan k-means color quantization (KMCQ) dan deteksi tepi structured forest. Proses selanjutnya melakukan pengurangan resolusi menggunakan metode pixelation dan binarization. Luaran dari algoritma menghasilkan 3 citra berbeda dengan ukuran yang sama, yaitu: citra tepi, citra biner, dan citra berwarna. Ketiga citra tersebut selanjutnya dilakukan proses pembentukan pola desain motif Karawo dengan berbagai petunjuk pola bagi pengrajin. Hasil menunjukkan bahwa pola desain motif dapat digunakan dan dimengerti oleh para pengrajin dalam menerapkannya di sulaman Karawo. Pengujian nilai-nilai parameter dilakukan pada metode k-means, gaussian filter, pixelation, dan binarization. Parameter-parameter tersebut yaitu: k pada k-means, kernel pada gaussian filter, lebar piksel pada pixelation, dan nilai threshold pada binarization. Pengujian menunjukkan nilai terendah tiap parameter adalah k=4, kernel=3x3, lebar piksel=70, dan threshold=20. Hasil memperlihatkan makin tinggi nilai-nilai tersebut maka semakin baik pola desain motif yang dihasilkan. Nilai-nilai tersebut merupakan nilai parameter terendah dalam pembentukan pola desain motif berkualitas baik berdasarkan indikator-indikator dari desainer. AbstractKarawo embroidery is a unique handicraft from Gorontalo. The embroidery motif is applied in detail based on a certain design pattern. These patterns are depicted on paper with various guides. The image applied to the pattern is very low resolution and retains its shape. This study develops a method to generate a Karawo design pattern from a digital image. The process begins by using k-means color quantization (KMCQ) to reduce the number of colors and edge detection of the structured forest. The next process is to change the resolution using pixelation and binarization methods. The output algorithm produces 3 different state images of the same size, which are: edge image, binary image, and color image. These images are used in the formation of the Karawo motif design pattern. The motif contains various pattern instructions for the craftsman. The results show that it can be used and understood by the craftsmen in its application in Karawo embroidery. Testing parameter values on the k-means method, Gaussian filter, pixelation, and binarization. These parameters are k on KMCQ, the kernel on a gaussian filter, pixel width in pixelation, and threshold value in binarization. The results show that the lowest value of each parameter is k=4, kernel=3x3, pixel width=70, and threshold=20. The results show that the higher these values, the better the results of the pattern design motif. Those values are the lower input to generate a good quality pattern design based on the designer’s indicators.
Penerapan Algoritma Naive Bayes Untuk Sistem Klasifikasi Status Gizi Bayi Balita Abas, Mohamad Ilyas; Lamusu, Rizal; Pranata, Widya Eka; Syahrial, Syahrial; Ibrahim, Irawan; Hasyim, Wahyudin; Kiayi, Verliana
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.508

Abstract

Infants and toddlers are in a critical period of rapid growth and development, often referred to as the "golden age." During this stage, regular nutritional assessments are essential to monitor health status and detect potential nutritional problems early. This study aims to classify the nutritional status of infants and toddlers using the Naïve Bayes algorithm, a probabilistic classification method based on Bayes' theorem with a strong assumption of attribute independence. The main attributes used in the classification system include age, weight, and height. The dataset consists of 700 records of infants and toddlers collected from previous observations. The results show that the Naïve Bayes algorithm can be effectively implemented for nutritional status classification, achieving a system accuracy of 88.14%. This indicates that the method performs well and has the potential to be utilized in decision support systems for child health monitoring.
Co-Authors Antupetu, Rastin Gobel, Alvian Van Ibrahim, Irawan Kango, Riklan Kiayi, Verliana Lasaruddin, Alter Lasarudin, Alter Lestari, Gita Maku, Rubiyanto Mohamad Ilyas Abas Nursetiawati Nursetiawati Pranata, Widya Eka Supu, Tahir Syahrial Syahrial SYAHRIAL SYAHRIAL Umar Sako, Umar Wahyudin Hasyim