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All Journal Jurnal Informatika JOIN (Jurnal Online Informatika) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Limits: Journal of Mathematics and Its Applications Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Advanced in Information and Industrial Technology (JAIIT) Humanism : Jurnal Pengabdian Masyarakat Jurnal Pengabdian Kepada Masyarakat Reputasi: Jurnal Rekayasa Perangkat Lunak International Journal of Data Science, Engineering, and Analytics (IJDASEA) Indonesian Community Journal The Indonesian Journal of Computer Science
Mohammad Hamim Zajuli Al Faroby
Telkom University
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Nursing Public Health Social Sciences Veterinary Other

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XGBoost and Network Analysis for Prediction of Proteins Affecting Insulin based on Protein Protein Interactions Mohammad Hamim Zajuli Al Faroby; Mohammad Isa Irawan; Ni Nyoman Tri Puspaningsih
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 5, No. 4, November 2020
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v5i4.1076

Abstract

Protein Interaction Analysis (PPI) can be used to identify proteins that have a supporting function on the main protein, especially in the synthesis process. Insulin is synthesized by proteins that have the same molecular function covering different but mutually supportive roles. To identify this function, the translation of Gene Ontology (GO) gives certain characteristics to each protein. This study purpose to predict proteins that interact with insulin using the centrality method as a feature extractor and extreme gradient boosting as a classification algorithm. Characteristics using the centralized method produces  features as a central function of protein. Classification results are measured using measurements, precision, recall and ROC scores. Optimizing the model by finding the right parameters produces an accuracy of  and a ROC score of . The prediction model produced by XGBoost has capabilities above the average of other machine learning methods.
XGB-Hybrid Fingerprint Classification Model for Virtual Screening of Meningitis Drug Compounds Candidate Mohammad Hamim Zajuli Al Faroby; Helisyah Nur Fadhilah; Siti Amiroch; Rahmat Sigit Hidayat
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 2, May 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i2.1424

Abstract

Meningitis is an infection of the lining of the brain caused by diffuse inflammation, and this condition is caused by viruses or bacteria that cause Meningitis. Prevention for this disease is still in the form of strengthening antibodies with vaccines. There is no significant compound to relieve or treat Meningitis patients. In previous studies, they got seven proteins vital to Meningitis. We continued to investigate the compounds associated with the seven proteins. We chose the in-silico process by utilizing data in an open database. We use several databases for the data collection process. After that, the compound data were extracted for bonding features and chemical elements using molecular fingerprints. We use two fingerprint methods, where both we combine with three types of combinations. The combined results produce three types of datasets with different matrix sizes. We establish the Extreme Gradient Boosting (XGB) method to form the classification model for the three datasets, select the best classification model, and compare it with other classification algorithms. The XGB model has better quality than the classification model of other algorithms. We used this model to predict and quantify compounds that strongly bind to seven vital meningitis proteins. The compound with the highest predictive score (we found more than 0.99) became a drug candidate to inhibit or neutralize Meningitis.
Regression Analysis of CAR, NPL-Net, LDR on Increasing Return on Asset (Case Study on Banking Companies Listed on IDX in 2018-2020) Mohammad Hamim Zajuli Al Faroby
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 2 No. 1 (2022): International Journal of Data Science, Engineering, and Analytics Vol 2, No 1,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v2i1.24

Abstract

Banking is a financial institution tasked with collecting funds from the public and then channeling them back to obtain income. The bank's performance can be seen by comparing the ratio figures in the annual financial statements that the bank has achieved. Therefore, this study aims to determine the CAR, NPL-Net, and LDR, which affect bank profitability or Return on Assets. This research method uses Multiple Linear Regression Analysis with secondary data and a ratio measurement scale, and the number of samples is 90 samples from 30 banking companies during the 2018-2020 period. This data is sourced from the Indonesia Stock Exchange, with the Judgment Sampling technique. The sample was reduced to 84 by the outlier test due to abnormal data. Based on the results of the analysis, it is known that CAR and LDR have no significant effect on Return on Assets, otherwise, NPL-Net has a significant effect on Return on Assets, and CAR, NPL-Net, LDR simultaneously have a significant effect on Return on Assets.
Classification of IGF1R ligand compounds for Identification of herbal extracts using extreme gradient boosting Mohammad Hamim Zajuli Al Faroby; Siti Amiroch; Bernadus Anggo Seno Aji; Avriono Aritonang
Jurnal Informatika Vol 16, No 3 (2022): September 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v16i3.a23286

Abstract

Diabetes Mellitus is a serious disease that requires serious treatment. The cause of this disease is due to malfunctions in insulin and insulin-producing organs. One of the proteins that become insulin signaling receptors is IGF1R, which has an important role in activating and maximizing insulin performance. In this study, we aimed to obtain herbal compounds that can activate the function of the IGF1R protein by utilizing compound data in an open database and modeling it using the ensemble method, namely extreme gradient boosting. We found that this method produces the best classification model than with other algorithms. We predicted 844 data for herbal compounds, but only 15 data met the threshold of 0.6. We got one plant from the fifteen herbal compounds, namely Zostera Marine, which was confirmed to have compounds that bind to IGF1R. These compounds have the highest probability value in the classification model that we formed compared to others.
Pendampingan Perizinan Usaha Melalui Online Single Submission Risk-Based Approach Untuk Pelaku Usaha Perempuan Sekitar Desa Pangkahwetan Kecamatan Ujungpangkah Rifdatun Ni'mah; Mohammad Hamim Zajuli Al Faroby; Bernadus Anggo Seno Aji
Humanism : Jurnal Pengabdian Masyarakat Vol 4 No 1 (2023): April
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/hm.v4i1.15720

Abstract

Kelompok perempuan merupakan pilar utama dalam pertumbuhan Usaha Mikro, Kecil dan Menengah (UMKM). Pemerintah telah melakukan sejumlah kebijakan untuk mendukung sektor UMKM dalam mengembangkan usaha diantaranya kemudahan perizinan usaha melalui Undang-Undang (UU) Nomor 11 Tahun 2020 tentang Cipta Kerja (Ciptaker). Undang-undang tersebut mendorong reformasi perizinan berusaha, salah satunya adalah bukti perizinan berusaha. Pelaku usaha dapat membuat Nomor Induk Berusaha (NIB) melalui sistem Online Single Submission Risk Based Approach (OSS-RBA). KPI Balai Perempuan Pangkahwetan sebagai kesatuan kelompok kepentingan perempuan di tingkat desa dan sekitarnya berupaya agar kelompok perempuan pelaku UMK di wilayah sekitar dapat mengurus perizinan berusaha supaya dapat meningkatkan kegiatan usaha mereka. Namun, sebagian besar dari kelompok perempuan ini berasal dari kelompok kurang mampu, usia lanjut, tingkat pendidikan dan literasi digital rendah. Kondisi tersebut membuat kelompok perempuan tersebut enggan mengurus perizinan usaha secara mandiri. Pengabdian masyarakat dilakukan agar mitra mendapatkan peningkatan pemberdayaan berupa pengetahuan terbaru terkait perizinan berusaha berbasis risiko melalui kegiatan penyuluhan dan pendampingan. Sebanyak 23 pelaku usaha telah berhasil terdaftar dalam sistem OSS-RBA dan mendapatkan dokumen NIB versi cetak secara gratis. Bukti legalitas usaha dapat dimanfaatkan untuk membantu dalam mengembangkan kapasitas usaha mereka.
Leaf Health Identification on Melon Plants Using Convolutional Neural Network Farah Zakiyah Rahmanti; Bernadus Anggo Seno Aji; Oktavia Ayu Permata; Berliana Amelia; M. Hamim Zajuli Al Faroby
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 1 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i1.58492

Abstract

Plants require complete nutrients to grow well and produce good-quality products. Some examples of symptoms in plants that lack nutrients such as wrinkled leaves and slow ripening of fruit, so plants are less productive. Plants that lack nutrients are unhealthy plants. This research aims to identify healthy and unhealthy leaves on melon plants so that immediate action can be taken to deal with them. This research will be useful for melon farmers everywhere. The dataset used is data taken directly using a digital camera with the help of melon farmers to label each data, both healthy and unhealthy leaves. This research has two main works, they are the training process and the testing process. The proposed research uses the Convolutional Neural Network (CNN) method with 10 epochs. The test results on the 20-test data achieve 100% accuracy. We used accuracy, precision, recall, and f1-score to evaluate the classification method.
Identifikasi Interaksi Protein-Protein Meningitis Menggunakan ClusterONE dan Analisis Jaringan Al Faroby, Mohammad Hamim Zajuli; Nur Fadhilah, Helisyah; Hartanta Sembiring, Fikri
Journal of Advances in Information and Industrial Technology Vol. 4 No. 1 (2022): May
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v4i1.180

Abstract

Patogen penyebab Meningitis menyebabkan peradangan pada selaput otak. Kondisi ini menyebabkan kondisi kronis jika dibiarkan dalam jangka waktu lama. Patogen Meningitis menyerang protein tertentu yang berkaitan langsung dengan fungsional selaput otak. Dengan mendeteksi protein signifikan dari Meningitis dapat kita dapat menganalisis lebih jauh untuk menemukan inhibitor dari patogen tersebut. Menemukan protein yang signifikan dengan menganalisis jaringan interaksi protein yang terlibat ketika meningitis menginfeksi. Data jaringan protein diolah untuk mendapatkan klaster protein yang signifikan. Data jaringan pada klaster terbaik digunakan untuk mencari protein tertentu yang memiliki pengaruh signifikan. Skor sigifikansi protein berdasarkan nilai karakterisktik simpul pada jaringan graf dengan mendapatkan nilai eigen dan vektor eigen. Nilai keseluruhan karakteristik diperoleh dari hasil perkalian vektor eigen dengan vektor karakteristik simpul yang menghasilkan nilai skalar. Kami menemukan protein yang paling signifikan terhadap Meningitis adalah TLR2, hal ini diketahui dari nilai keseluruhan karakteristiknya yang paling tinggi dibandingkan protein lainnya.
Target Baru Pengobatan Meningitis berdasarkan Centrality Measure jaringan protein dan Self Oganizing Map. Amiroch, Siti; Hamim Zajuli al Faroby, Mohammad; Dzulfikar Fauzi, Mohammad
Limits: Journal of Mathematics and Its Applications Vol 21, No 3 (2024)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v21i3.21776

Abstract

Meningitis is a serious threat to health with potentially fatal consequences. Understanding protein interactions related to chronic conditions is crucial for the development of effective treatments. In silico analysis is considered to have greater effectiveness because it simulates through computation and tries various possibilities at a lower cost. This study aims to analyze protein-protein interactions related to Meningitis with cluster analysis techniques on undirected graphs. The proposed method is the Self Organizing Map (SOM) algorithm as a cluster. This algorithm can cluster undirected graph-based protein interaction data. Protein data involved in Meningitis disease comes from OMIM. From this data, proteins belonging to the gene locus are explored for their interactions, resulting in interaction data in the form of an undirected graph. The combination of centrality measure is used for feature engineering on undirected graph data. The main protein candidates are potentially located in the Cluster 1 model with the largest silhouette score (0.359) and Davies-Bouldin Index (1.667). The cluster has 18 proteins with the highest significance to Meningitis. From the overall centrality ranking results, the three highest significance proteins are CISH (3.921222), TNFSF10 (3.403541), and ICAM3 (2.623702) which have the potential to become Meningitis target proteins. CISH protein has the highest overall centrality score value compared to the others, so CISH protein may be a new alternative in the treatment of Meningitis.
Smart Irrigation untuk Optimalisasi Pertanian Sistem Green House pada Kelompok Petani Tani Sejahtera di Desa Temuasri, Banyuwangi Al Faroby, Mohammad Hamim Zajuli; Fadhilah, Helisyah Nur; Permata, Regita Putri; Kamali, Muhammad Adib
I-Com: Indonesian Community Journal Vol 5 No 1 (2025): I-Com: Indonesian Community Journal (Maret 2025)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/icom.v5i1.6540

Abstract

Teknologi irigasi pintar adalah inovasi yang bertujuan meningkatkan efisiensi penggunaan air dan pupuk dalam pertanian, khususnya di lingkungan greenhouse. Salah satu implementasinya dilakukan di Temuasri Greenhouse, pusat budidaya melon dengan sistem tabulampot, yang kini memanfaatkan smart irrigation untuk mengoptimalkan proses irigasi. Sistem ini bekerja dengan mengandalkan Programmable Logic Controller (PLC) dan Human-Machine Interface (HMI) yang memungkinkan penyiraman dan distribusi pupuk berjalan otomatis serta dapat dipantau secara real-time. Untuk memastikan sistem ini dapat diterapkan dengan baik, sebanyak 16 peserta dilibatkan dalam pelatihan mengenai instalasi, pengoperasian, dan pemeliharaan teknologi ini. Hasil evaluasi menunjukkan bahwa penerapan smart irrigation mampu meningkatkan efisiensi penggunaan air hingga 40% dan menghemat waktu kerja manual hingga 50%, sekaligus mengurangi kontak langsung selama proses penyiraman. Survei juga mencatat 84,72% peserta memberikan umpan balik sangat positif terhadap program ini. Dengan keberhasilan tersebut, implementasi smart irrigation diharapkan menjadi model pengelolaan irigasi berbasis teknologi yang berkelanjutan, membantu meningkatkan produktivitas, serta mendukung kualitas hasil pertanian di masa depan.
Modality-based Modeling with Data Balancing and Dimensionality Reduction for Early Stunting Detection Setiawan, Yohanes; Al Faroby, Mohammad Hamim Zajuli; Ma’ady, Mochamad Nizar Palefi; Sanjaya, I Made Wisnu Adi; Ramadhani, Cisa Valentino Cahya
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1495

Abstract

In Indonesia, the stunting rate has reached 36%, significantly higher than the World Health Organization's (WHO) standard of 20%. This high prevalence underscores the urgent need for effective early detection methods. Traditional data mining approaches for stunting detection have primarily focused on unimodal data, either tabular or image data alone, limiting the comprehensiveness and accuracy of the detection models. Modality-based modeling, which integrates image and tabular data, can provide a more holistic view and improve detection accuracy. This research aims to analyze modality-based modeling for the early detection of stunting. Two modalities, unimodal and multimodal, are used in this study. The main contributions of this research are the development of a comprehensive framework for modality-based analysis, the application of advanced data preprocessing techniques, and the comparison of various machine learning algorithms to identify the best model for stunting detection. The dataset, comprising images and tabular data, is sourced from Posyandu in Sidoarjo, Indonesia. Image data undergoes preprocessing, including background segmentation and feature extraction using the Gray Level Co-occurrence Matrix (GLCM), while tabular data is processed through categorical encoding. The Synthetic Minority Oversampling Technique (SMOTE) addresses class imbalance, and Principal Component Analysis (PCA) is used for dimensionality reduction. Unimodal modeling uses tabular or image data alone, while multimodal modeling combines both before classification. The study achieves the best F1 scores of 0.96, 0.91, and 0.90 for tabular-only, image-only, and image-tabular modalities, respectively, demonstrating the effectiveness of data balancing and dimensionality reduction techniques.
Co-Authors Ardian Yusuf Wicaksono Avriono Aritonang Berlian Rahmy Lidiawaty Berliana Amelia Bernadus Anggo Seno AjiAji Dzulfikar Fauzi, Mohammad Farah Zakiyah Rahmanti Fauzi, Muhammad Dzulfikar Granita Hajar Hartanta Sembiring, Fikri Helisyah Nur Fadhilah Maulana, Rizky Fenaldo Mochamad Nizar Palefi Ma’ady Mohammad Isa Irawan Muhammad Adib Kamali Ni Nyoman Tri Puspaningsih Oktavia Ayu Permata Rahmat Sigit Hidayat Ramadhani, Cisa Valentino Cahya Ramaputra, Chrisdion Andrew Regita Putri Permata Rifdatun Ni’mah Sanjaya, I Made Wisnu Adi Siti Amiroch, Siti Yohanes Setiawan