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All Journal El-QUDWAH Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics CESS (Journal of Computer Engineering, System and Science) Bulletin of Electrical Engineering and Informatics Journal of Information Technology and Computer Science (JOINTECS) Applied Information System and Management Conference on Innovation and Application of Science and Technology (CIASTECH) Jurnal Riset Informatika Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Scientica: Jurnal Ilmiah Sains dan Teknologi
Muhammad Faisal
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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K-Means Binary Search Centroid with Dynamic Cluster for Java Island Health Clustering Muhammad Andryan Wahyu Saputra; Muhammad Faisal; Ririen Kusumawati
Jurnal Riset Informatika Vol. 5 No. 3 (2023): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (932.207 KB) | DOI: 10.34288/jri.v5i3.218

Abstract

This study is focused on determining the health status of each district/city in Java using the K-means Binary Search Centroid and Dynamic Kmeans algorithms. The research data uses data on the health profile of Java Island in 2020. Comparative algorithms were tested using the Davies Bound Index and Calinski-Harabasz Index methods on the traditional k-means algorithm and dynamic binary search centroid k-means. Based on the test, 5 clusters were found in the distribution area, including 11 regions with very high health quality cluster 1, 24 regions with high health quality, 28 regions with moderate health quality, and 28 clusters 4 with low health quality, 45 regions, and cluster 5 with poor health quality is 11 regions, with the best validation value of DBI 1.8175 and CHI 67.7868. Overall optimization of the dynamic k-means algorithm based on binary search centroid results in a better average cluster quality and a smaller number of iterations than the traditional k-means algorithm. The test results can be used as one of the best methods in evaluating the level of health in the Java Island area and a reference for decision-making in determining policies for related agencies.
Performance Improvement of K-Nearest Neighbor Algorithm in KIP Scholarship Recipient Selection Manzilur Rahman Romadhon; M. Faisal; M. Imamudin
Jurnal Riset Informatika Vol. 5 No. 4 (2023): September 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i4.242

Abstract

Abstract Law 12 of 2012 mandates that the government increase access to higher education for high achievers and underprivileged people. One of the efforts to realize this is by providing KIP Lectures. To ensure that beneficiaries are indeed eligible for KIP scholarships, it is necessary to classify scholarship recipients with data mining classification techniques correctly. The classification technique chosen is k-Nearest Neighbor (K-NN). K-NN is a classification method that relies heavily on the k parameter in carrying out classification. K-NN was applied to the KIP Scholarship applicant dataset at UIN Malang in 2022. The test scenario in this research is to compare the k-odd and k-even parameters to find the most optimal k value in K-NN. The highest accuracy value obtained by k-odd is 0.71 or 71% when k=9, and the highest for k-even is 0.67 or 67% when k=10. Using optimal k parameters is proven to improve k-NN performance. The K-NN algorithm with k-odd parameters, namely k=9, is the best method for classifying KIP scholarship recipients in this research. The results of this research can be considered in determining KIP scholarship recipients worthy of using K-NN.
ANALISIS PERBANDINGAN KECEPATAN ALGORITMA SELECTION SORT DAN BUBBLE SORT Nanang Mahrozi; Muhammad Faisal
Scientica: Jurnal Ilmiah Sains dan Teknologi Vol. 1 No. 2 (2023): Scientica: Jurnal Ilmiah Sains dan Teknologi
Publisher : Komunitas Menulis dan Meneliti (Kolibi)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.572349/scientica.v1i2.209

Abstract

Algorithms play a crucial role in software development and programming. Many programming paradigms have been developed to understand the basic concepts of algorithms, both general and specific. Data processing is essential in the sorting algorithm, especially in the context of selection sort and bubble sort. Both algorithms are designed to sort data with integer types. Each type of algorithm has different levels of effectiveness. The effectiveness of an algorithm can be measured based on the time and memory space needed to execute it. An efficient algorithm is one that optimizes the use of time and space minimally. The fewer resources needed to run an algorithm, the more effective it is considered. It is important to note that the time and space required by an algorithm are influenced by the amount of data processed and the type of algorithm applied, which is the main focus of this article, with an emphasis on the time complexity of various types of algorithms. The algorithms described in this study are implemented using the Python programming language in the Google Colab environment. Referring to the logic of the data sorting process using algorithms such as Selection Sort and Bubble Sort, it can be concluded that the selection sort algorithm has a speed advantage over bubble sort, as evidenced by testing with 100-100,000 data where selection sort is consistently faster.
Penerapan Long Short-Term Memory untuk Klasifikasi Multi-Label Terjemahan Al-Qur’an dalam Bahasa Indonesia Ismail Akbar; Muhammad Faisal; Totok Chamidy
JOINTECS (Journal of Information Technology and Computer Science) Vol 8, No 1 (2024)
Publisher : Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31328/jointecs.v8i1.5291

Abstract

Mempelajari Al-Qur'an adalah salah satu ibadah yang sangat dianjurkan dalam Islam, dan memahami cara mengklasifikasikan ayat-ayatnya sangat penting. Pengelompokan ayat-ayat ini mempermudah pencarian dan mempelajari ayat-ayat terkait, karena setiap ayat dapat termasuk dalam satu atau lebih kategori yang berbeda. Penelitian ini bertujuan untuk mengembangkan model klasifikasi multi-label untuk teks terjemahan Al-Qur’an dalam bahasa Indonesia menggunakan Bi-Directional Long Short-Term Memory (Bi-LSTM) dan teknik word embedding Word2Vec dengan arsitektur Continuous Bag of Words (CBOW). Data yang digunakan berasal dari terjemahan resmi Kementerian Agama Republik Indonesia, khususnya dari Surah An-Nisa', Surah Al-Maidah, dan Surah Al-An'am, dengan total 461 ayat. Proses klasifikasi mempertimbangkan empat kategori utama: Tauhid, Ibadah, Akhlaq, dan Sejarah (Tarikh). Model diuji dengan berbagai skenario pembagian data dan dievaluasi menggunakan metrik akurasi, precision, recall, dan Hamming Loss. Hasil menunjukkan bahwa model Bi-LSTM dengan Word2Vec mencapai akurasi 70,21%, precision 64,31%, recall 61,13%, dan Hamming Loss 36,52%. Meskipun menunjukkan peningkatan signifikan, model ini masih menghadapi tantangan dalam mencapai akurasi yang lebih tinggi karena kompleksitas bahasa Al-Qur’an. Penelitian ini menyarankan penggunaan data yang lebih representatif, teknik embedding lain, dan arsitektur model yang lebih canggih untuk perbaikan lebih lanjut. Model ini diharapkan dapat mempermudah pembelajaran dan pemahaman Al-Qur’an secara lebih akurat dan efisien.
Co-Authors Cahyo Crysdian Fresy Nugroho Fresy Nugroho Ida Ayu Putu Sri Widnyani Ismail Akbar M. Imamudin Manzilur Rahman Romadhon Muhammad Aji Permana Muhammad Andryan Muhammad Andryan Wahyu Saputra Muhammad Sahi Nanang Mahrozi Ririen Kusumawati Suhartono Suhartono Suyanta Suyanta Tamaulina Br Sembiring Totok Chamidy Wikke Alvina Medyanti Yunifa Miftachul Arif