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All Journal Jurnal Informatika JOIN (Jurnal Online Informatika) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Eksakta : Berkala Ilmiah Bidang MIPA Journal of Advanced in Information and Industrial Technology (JAIIT) Journal of Applied Data Sciences Humanism : Jurnal Pengabdian Masyarakat Jurnal Abdimas Indonesia : Jurnal Abdimas Indonesia Jurnal Pengabdian Kepada Masyarakat Reputasi: Jurnal Rekayasa Perangkat Lunak International Journal of Data Science, Engineering, and Analytics (IJDASEA) Indonesian Community Journal Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence The Indonesian Journal of Computer Science Limits: Journal of Mathematics and Its Applications
Mohammad Hamim Zajuli Al Faroby
Telkom University
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Analisis Sentimen Ulasan Pengguna pada Aplikasi Cryptocurrency: Evaluasi Dampak Skenario Pembagian Dataset Menggunakan Multinomial Naive Bayes Ramaputra, Chrisdion Andrew; Al Faroby, Mohammad Hamim Zajuli; Lidiawaty, Berlian Rahmy
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4263

Abstract

The surge in cryptocurrency investors in Indonesia, reaching 18.83 million by January 2024, signifies an expanding interest in this market. This research conducts a sentiment analysis of user reviews on Indodax and Tokocrypto, the premier cryptocurrency trading platforms in Indonesia. Utilizing the Multinomial Naive Bayes method, the study examines the influence of various dataset split scenarios and random states on the model's performance. The findings reveal substantial variability in the model's accuracy based on different random states and test sizes. Notably, the Positive sentiment label consistently shows high-performance metrics, while the Neutral label underperforms. These insights are invaluable for developers aiming to improve user experience and for investors seeking to make informed decisions. This research underscores the significance of sentiment analysis in understanding user interactions and enhancing the credibility of cryptocurrency investment platforms.
Simulasi Pengiriman Air Mineral Galon dengan Multi Depot Menggunakan Hill Climbing dan Algoritma A* Fauzi, Muhammad Dzulfikar; Hajar, Granita; Nur Rachmaniar, Desita; Hamim Zajuli al Faroby, Mohammad
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 2 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i2.1170

Abstract

Air mineral sangat penting bagi kesehatan manusia karena berperan dalam hidrasi tubuh, menjaga keseimbangan cairan, dan mendukung fungsi organ serta sistem tubuh. Untuk memenuhi kebutuhan sehari-hari, distribusi dari depo air ke pelanggan harus mempertimbangkan rute tercepat agar biaya pengiriman efisien. Mencari rute terpendek dengan alokasi depo terbaik dengan tetap mempertimbangkan keterbatasan kapasitas masing-masing depo dan kendaraan merupakan tujuan dari penelitian ini. Algoritma Hill Climbing merupakan metode yang efektif untuk menentukan rute terdekat antar titik pengiriman. Selain itu, algoritma A* dapat digunakan untuk mencari rute optimal dengan menggunakan informasi tambahan (heuristik) yang mengarahkan pencarian ke jalur yang paling efisien. Berdasarkan hasil penelitian, 450,41 merupakan rute terjauh dengan menggunakan moda transportasi penjemputan dengan satu depo. Sedangkan untuk moda transportasi penjemputan dengan dua depo, jalur terpendek adalah 257,62; sedangkan untuk menggunakan sepeda motor atau kereta api, jalur terpendek adalah 271,75.
Smart Irrigation untuk Optimalisasi Pertanian Sistem Green House pada Kelompok Petani Tani Sejahtera di Desa Temuasri, Banyuwangi Mohammad Hamim Zajuli Al Faroby; Helisyah Nur Fadhilah; Regita Putri Permata; Muhammad Adib Kamali
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

Smart irrigation technology is an innovation designed to improve the efficiency of water and fertilizer management in agricultural activities, especially in a greenhouse environment. This study aims to implement and evaluate the use of smart irrigation technology in Temuasri Greenhouse, which is known as one of the centers of melon cultivation with tabulampot system (fruit plants in pots). The system relies on a Programmable Logic Controller (PLC) and Human-Machine Interface (HMI) to automate the watering process and fertilizer distribution and facilitate real-time monitoring of irrigation conditions. The program involved 16 target communities who received training on the installation, operation, and maintenance of the smart irrigation system. Based on the evaluation results, the technology was successfully implemented, providing increased efficiency in water and labor use, while reducing direct contact during the watering process. The survey showed that 84.72% of participants gave very positive feedback, stating that this activity was effective, useful, and has great potential for further development. The successful implementation of smart irrigation technology is expected to become a reference for technology-based irrigation management models that support agribusiness sustainability, while increasing the productivity and quality of agricultural products in the future.
Target Baru Pengobatan Meningitis berdasarkan Centrality Measure jaringan protein dan Self Oganizing Map. Siti Amiroch; Mohammad Hamim Zajuli Al Faroby; Muhammad Dzulfikar Fauzi
Limits: Journal of Mathematics and Its Applications Vol. 21 No. 3 (2024): Limits: Journal of Mathematics and Its Applications Volume 21 Nomor 3 Edisi No
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Sistem Pendukung Keputusan untuk Identifikasi Protein Kunci pada Kanker Darah: Integrasi MCDM, KMeans, dan Topologi Jaringan Mohammad Hamim Zajuli Al Faroby; Muhammad Dzulfikar Fauzi
Reputasi: Jurnal Rekayasa Perangkat Lunak Vol. 6 No. 1 (2025): Mei 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/reputasi.v6i1.8959

Abstract

Kanker darah merupakan salah satu penyakit kompleks yang dipicu oleh gangguan pada regulasi jalur pensinyalan seluler, salah satunya melibatkan mutasi pada protein JAK2. Mengingat pentingnya JAK2 dalam patogenesis kanker hematologi, diperlukan pendekatan sistemik berbasis data untuk mengidentifikasi protein-protein yang memiliki asosiasi fungsional dengannya secara menyeluruh. Penelitian ini bertujuan mengembangkan sistem pendukung keputusan (SPK) untuk mengidentifikasi protein kunci dalam jaringan interaksi protein (PPI) terkait JAK2, melalui integrasi algoritma KMeans clustering, fitur topologi graf, dan metode Multi-Criteria Decision Making (MCDM). Data PPI diperoleh dari STRING-DB dan divisualisasikan menggunakan Cytoscape. Delapan fitur topologi jaringan diekstraksi sebagai dasar analisis, di antaranya degree, stress, dan neighborhood connectivity. Proses klasterisasi menghasilkan tiga kelompok optimal yang divalidasi menggunakan Silhouette Score dan Davies-Bouldin Index. Selanjutnya, model MCDM diterapkan untuk mengevaluasi kontribusi masing-masing klaster secara agregat terhadap kestabilan jaringan. Hasil penelitian menunjukkan bahwa Cluster 2 memiliki skor MCDM tertinggi (0,6667), ditopang oleh nilai stress dan degree yang sangat tinggi, mengindikasikan peran strategis protein dalam klaster tersebut sebagai hub utama dalam jaringan. Temuan ini memberikan landasan kuat untuk eksplorasi kandidat target terapi baru yang potensial dalam konteks kanker darah, serta menegaskan efektivitas integrasi teknik analisis graf dan SPK berbasis MCDM dalam studi jaringan molekuler.
Newton Divided Difference Optimization for Fingerprint-Based Neural Virtual Screening against Avian Influenza A/H9N2 Siti Amiroch; Mohammad Jamhuri; Awawin Mustana Rohmah; Mohammad Hamim Zajuli Al Faroby; Chairul Anwar Nidom; Reviany Vibrianita Nidom
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1416

Abstract

Avian influenza A/H9N2 poses persistent zoonotic and veterinary threats, yet efficient computational tools for antiviral compound prioritization remain underdeveloped, particularly with respect to optimizer behavior in high-dimensional neural screening. This study proposes Newton Divided Difference (NDD) optimization, a lightweight positive diagonal curvature-aware training strategy, as a novel optimizer for fingerprint-based neural virtual screening against avian influenza A/H9N2, with the objective of evaluating its performance across aligned molecular fingerprint representations and chemically structured validation protocols. An aligned benchmark of 1,459 molecules consisting of 615 candidate active compounds and 844 decoys was represented by EState (79 features), PubChem (881 features), and Klekota–Roth (4,860 features) fingerprints, sharing identical molecule identities, labels, and split assignments. A fixed multilayer perceptron (MLP) classifier was trained with NDD and seven baseline optimizers under stratified, scaffold-key, and similarity-cluster split protocols across five repeated seeds. NDD achieved the highest descriptive ROC-AUC (Receiver Operating Characteriztic – Area Under the Curve) and PR-AUC (Precision-Recall Area Under the Curve) on Klekota–Roth fingerprints under scaffold-key and similarity-cluster protocols, and remained competitive under the stratified split with ROC-AUC of 0.9872. Architecture-sensitivity tests confirmed stable NDD performance across multiple network configurations, with ROC-AUC values ranging from 0.9876 to 0.9890. Compared with Hessian-free optimization, NDD reduced per-run runtime from approximately 50–54 seconds to approximately 8 seconds on Klekota–Roth under the same CPU-only configuration while achieving comparable ranking performance. The novelty of this work lies in the first systematic assessment of NDD for H9N2 neural virtual screening, demonstrating that positive diagonal curvature-aware scaling provides a practical, stable, and computationally efficient optimization alternative in sparse high-dimensional ligand-based screening settings, although external validation and prospective experimental confirmation remain necessary before practical antiviral prioritization.
In Silico Study of the Potential of Bioactive Compounds of Theobroma cacao as Antigingivitis Agents Amalia Putri Lubis; Hidayanti Hidayanti; Rahadian Zainul; Iffat Syafiqoh Afif; Siti Amiroch; Mohammad Jamhuri; Mohammad Hamim Zajuli Al Faroby
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 27 No. 04 (2026): Eksakta : Berkala Ilmiah Bidang MIPA (E-ISSN : 2549-7464) In Progress
Publisher : Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Negeri Padang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/eksakta/vol27-iss04/674

Abstract

This in silico study evaluated the antigingivitis potential of bioactive compounds from Theobroma cacao against matrix metalloproteinase-8 (MMP-8) (PDB ID: 5T35) using molecular docking and ADMET prediction. Molecular docking results showed that several compounds exhibited stronger binding affinities, indicated by lower docking scores, compared to the reference compound doxycycline. Among the evaluated ligands, 5,7-dihydroxy-3,4-dimethoxyflavone and procyanidin B1 demonstrated the most favorable binding interactions, primarily stabilized by hydrogen bonding and hydrophobic contacts within the active site. However, drug-likeness and ADMET analyses revealed that large polyphenolic compounds, particularly procyanidin B1, violated multiple pharmacokinetic criteria, suggesting limited oral bioavailability. In contrast, 5,7-dihydroxy-3,4-dimethoxyflavone showed an optimal balance between binding affinity and pharmacokinetic properties, with full compliance with Lipinski’s Rule of Five and Veber’s criteria, high predicted gastrointestinal absorption, and a favorable toxicity profile. Overall, this compound emerged as the most promising antigingivitis candidate from Theobroma cacao. These findings provide valuable molecular insights and support further experimental validation for the development of plant-based therapeutic agents targeting gingivitis..
Pengenalan Visualisasi Data untuk Meningkatkan Pemahaman Analisis Data Siswa SMAU BP Amanatul Ummah Mojokerto Auliya Ardhini Putri; Ayunda Dewi Agustin; Era Estiana; Nur Nisrina Salsabilla; Rifdatun Nimah; Mohammad Hamim Zajuli Alfaroby
Jurnal Abdimas Indonesia Vol. 6 No. 2 (2026)
Publisher : Perkumpulan Dosen Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34697/jai.v6i2.2931

Abstract

Perkembangan teknologi digital menuntut siswa sekolah menengah untuk memiliki kemampuan literasi data sebagai bekal berpikir kritis dan pengambilan keputusan berbasis data. Namun, kemampuan analisis dan visualisasi data masih belum banyak diperkenalkan secara praktis di lingkungan sekolah. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pemahaman analisis data melalui pengenalan visualisasi data menggunakan perangkat lunak Tableau kepada siswa Sekolah Menengah Atas Unggulan Berbasis Pesantren Amanatul Ummah Mojokerto. Metode pelaksanaan kegiatan dilakukan secara partisipatif edukatif melalui tahapan persiapan materi, penyampaian konsep literasi data, demonstrasi penggunaan Tableau, praktik pembuatan grafik dan dashboard interaktif, diskusi, serta evaluasi menggunakan pretest, posttest, dan kuesioner umpan balik. Kegiatan ini melibatkan 25 siswa sebagai peserta utama yang mengikuti seluruh rangkaian pelatihan. Hasil evaluasi menunjukkan adanya peningkatan pemahaman yang signifikan, ditandai dengan kenaikan nilai rata-rata peserta dari 66,35 pada pretest menjadi 97,55 pada posttest. Seluruh peserta mampu membuat visualisasi data dasar dan menyusun dashboard sederhana serta menunjukkan peningkatan kemampuan dalam menginterpretasikan pola data. Manfaat kegiatan dirasakan langsung oleh peserta melalui peningkatan literasi data, literasi digital, dan kepercayaan diri dalam memanfaatkan data sebagai dasar pengambilan keputusan sederhana. Kegiatan ini menunjukkan bahwa pelatihan visualisasi data berbasis praktik dengan keterlibatan aktif masyarakat sekolah merupakan pendekatan yang efektif dan inovatif dalam memperkuat literasi data pada jenjang pendidikan menengah.
Co-Authors Amalia Putri Lubis Ardian Yusuf Wicaksono Auliya Ardhini Putri Avriono Aritonang Awawin Mustana Rohmah Ayunda Dewi Agustin Berlian Rahmy Lidiawaty Berliana Amelia Bernadus Anggo Seno AjiAji Chairul Anwar Nidom Dzulfikar Fauzi, Mohammad Era Estiana Farah Zakiyah Rahmanti Granita Hajar Hartanta Sembiring, Fikri Helisyah Nur Fadhilah Helisyah Nur Fadhilah Hidayanti Hidayanti Iffat Syafiqoh Afif Mochamad Nizar Palefi Ma’ady Mohammad Isa Irawan Mohammad Jamhuri Mohammad Jamhuri Muhammad Adib Kamali Muhammad Dzulfikar Fauzi Ni Nyoman Tri Puspaningsih Nur Nisrina Salsabilla Nur Rachmaniar, Desita Oktavia Ayu Permata Rahadian Zainul Rahmat Sigit Hidayat Ramadhani, Cisa Valentino Cahya Ramaputra, Chrisdion Andrew Regita Putri Permata Reviany Vibrianita Nidom Rifdatun Nimah Rifdatun Ni’mah Rizky Fenaldo Maulana Sanjaya, I Made Wisnu Adi Siti Amiroch Siti Amiroch Siti Amiroch Siti Amiroch, Siti Yohanes Setiawan