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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 Jurnal Teknik Elektro (JTE) 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.
Analysis of the Use of Artificial Neural Network Models in Predicting Bitcoin Prices Muhammad Sahi; Muhammad Faisal; Yunifa Miftachul Arif; Cahyo Crysdian
Applied Information System and Management (AISM) Vol. 6 No. 2 (2023): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v6i2.29648

Abstract

Bitcoin is one of the fastest-growing digital currencies or cryptocurrencies in the world. However, the highly volatile Bitcoin price poses a very extreme risk for traders investing in cryptocurrencies, especially Bitcoin. To anticipate these risks, a prediction system is needed to predict the fluctuations in cryptocurrency prices. Artificial Neural Network (ANN) is a relatively new model discovered and can solve many complex problems because the way it works mimics human nerve cells. ANN has the advantage of being able to describe both linear and non-linear models with a fairly wide range. This research aims to determine the best performance and level of accuracy of the ANN model using the Back-Propagation Neural Network (BPNN) algorithm in predicting Bitcoin prices. This study uses Bitcoin price data for the period 2020 to 2023 taken from the CoinDesk market. The results of this study indicate that the ANN model produces the best performance in the form of four input nodes, 12 hidden nodes, and one output node (4-12-1) with an accuracy rate of around 3.0617175%.
Struggling Models: An Analysis of Logistic Regression and Random Forest in Predicting Repeat Buyers with Imbalanced Performance Metrics Siska Farizah Mauludiah; Yunifa Miftachul Arif; Muhammad Faisal; Dony Darmawan Putra
Applied Information System and Management (AISM) Vol. 7 No. 2 (2024): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v7i2.39326

Abstract

Predicting repeat buyers is essential for businesses seeking to improve customer retention and maximize profitability. This study examines the effectiveness of logistic regression and random forest algorithms in forecasting repeat buyers, utilizing an e-commerce dataset from Kaggle. Despite the theoretical strengths of these models, our results indicate significant performance challenges. Both models were evaluated on key metrics: accuracy, precision, recall, F1 score, and ROC-AUC. The findings revealed that the models logistic regression and random forest performed poorly, with accuracy hovering around 50%, precision and recall demonstrating imbalanced performance, and ROC-AUC scores barely exceeding random guessing levels. Such metrics highlight the limited discriminative power of these models in identifying repeat buyers. The analysis suggests that issues such as data quality, feature relevance, and class imbalance contribute to these shortcomings. Specifically, the models struggled to effectively learn from the data, leading to suboptimal predictions. These results underscore the need for enhanced feature engineering, better handling of class imbalance, and possibly exploring more advanced algorithms. This study provides a critical assessment of the limitations inherent in using Logistic Regression and Random Forest for predicting repeat buyers, hence implements feature engineering, SMOTE and hyperparameter tuning using RandomSearchCV to get better result.
Memulihkan Reproduksibilitas Bit-Eksak pada Benchmark Steganalisis di bawah Kompresi JPEG Berantai dan Derau Gauss melalui Pemulihan Salt Hash dan Audit Artefak Pelatihan Nurdin Wibisono; Muhammad Faisal; Fresy Nugroho; Yunifa Miftachul Arif; Imam Ashar; Suko Wiyanto; Nursih Dwi Hastuti
JURNAL TEKNIK ELEKTRO Vol 15 No 3 (2026): Vol 15 No 3 (2026): SEPTEMBER 2026
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jte.v15n3.p313-327

Abstract

Klaim ketahanan untuk steganalyzer deep learning hanya dapat dipercaya sejauh citra benchmark tempat klaim itu diukur dapat dipercaya, padahal dataset steganalisis jarang diperiksa dari segi regenerasi bit-eksak, konsistensi artefak pelatihan, maupun statistik yang dapat dihitung ulang. Penelitian ini membangun benchmark berpasangan dan terpisah per sumber (source-disjoint) dengan 13.000 citra (2.500 sumber BOSSbase, Adaptive LSB-MMED pada 0,2 bpp, sembilan kondisi JPEG dan derau Gauss tunggal maupun berantai), dan merilisnya bersama manifes SHA-256, metadata Croissant 1.0 yang tervalidasi, sembilan eksekusi detektor baseline, serta protokol reproduksibilitas empat tingkat yang mencakup integritas berkas, regenerasi bit-eksak, audit artefak pelatihan, dan penghitungan ulang metrik. Seluruh 12.200 berkas yang diaudit cocok dengan manifes, dan pembagian data, seluruh 2.100 hasil penyisipan yang tersedia, serta kedua kondisi JPEG teregenerasi identik byte demi byte. Sebaliknya, keenam kondisi derau meregenerasi 0% berkasnya pada setiap pengaturan interpreter, karena seed derau bergantung pada hash string Python yang diacak per proses. Pencarian menyeluruh atas 65.536 kemungkinan salt 16-bit mengidentifikasi satu salt unik per kondisi hanya dari lima sampai tujuh piksel, tidak cocok dengan nilai PYTHONHASHSEED mana pun hingga 1,2 × 107, dan memulihkan regenerasi identik byte demi byte atas seluruh 6.000 berkas ber-derau. Realisasi derau alternatif mengubah AUC detektor terbaik dalam rentang 0,010. Audit artefak meloloskan sembilan eksekusi yang dirilis dan mendeteksi keempat kesalahan yang disuntikkan, sedangkan penghitungan ulang metrik dari prediksi per citra mengoreksi kesalahan agregasi satu-seed dan uji DeLong yang mengabaikan kovarians, sehingga membalik satu keputusan signifikansi. Oleh karena itu, seed tetap dan kode yang dipublikasikan tidak cukup; seed harus menghindari hash ber-salt, dan regenerasi harus diverifikasi pada tingkat byte.
Co-Authors Cahyo Crysdian Dony Darmawan Putra Fresy Nugroho Fresy Nugroho Ida Ayu Putu Sri Widnyani Imam Ashar Ismail Akbar M. Imamudin Manzilur Rahman Romadhon Muhammad Aji Permana Muhammad Andryan Muhammad Andryan Wahyu Saputra Muhammad Sahi Nanang Mahrozi Nurdin Wibisono Nursih Dwi Hastuti Ririen Kusumawati Siska Farizah Mauludiah Suhartono Suhartono Suko Wiyanto Suyanta Suyanta Tamaulina Br Sembiring Totok Chamidy Wikke Alvina Medyanti Yunifa Miftachul Arif