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All Journal JOIN (Jurnal Online Informatika) Jurnal Mirai Management JOIV : International Journal on Informatics Visualization JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Syntax Literate: Jurnal Ilmiah Indonesia Jurnal Teknoinfo JSiI (Jurnal Sistem Informasi) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Informasi dan Pendidikan Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Indonesia : Manajemen Informatika dan Komunikasi JURNAL REKAYASA INFORMASI SWADHARMA (JRIS) Prosiding Konferensi Nasional PKM-CSR eProceedings of Engineering Jurnal INFOTEL Journal of Community Service and Society Empowerment Jurnal Indonesia : Manajemen Informatika dan Komunikasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) The Indonesian Journal of Computer Science SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan
Faqih Hamami
Telkom University, Bandung
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Nursing Public Health Social Sciences Other

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ARCHITECTURE DESIGN OF HEALTH ASSET DETECTION SYSTEM IN HOSPITAL Widyadhari, Dinda Putri; Sinung Suakanto; Faqih Hamami; Anis Farihan Mat Raffei
Jurnal Sistem Informasi Vol 11 No 2 (2024)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v11i2.9135

Abstract

Efficient management of hospital assets is essential to ensure that operations can run optimally and the quality of health services is good. However, the recording and management of assets in hospitals carried out manually often causes data errors, information mismatches, and also assets are only known by the manager without being explicitly recorded. In overcoming this problem, the researcher aims to develop a hospital asset detection system architecture using an iterative and incremental methodology approach. The stages of this system development include identification of needs and conceptual models, logical architecture design, conceptual architecture design, logical architecture, physical architecture, technology selection, and evaluation. This system utilizes YOLO model reading technology for asset detection and identification, storing detection results into a local database using SQLite3, sending data to a central server via API, and post-processing data by selecting the highest confidence score stored in a MySQL database and then using the data to manage asset management and asset visualization. The implementation of this system successfully reduces manual recording time, improves asset visibility, and optimizes resource usage, thus contributing to the improvement of efficiency and quality of health services.
KLASIFIKASI SOAL MENGGUNAKAN MULTI-LABEL PROBLEM TRANSFORMATION DENGAN METODE RANDOM FOREST DAN K-NEAREST NEIGHBOR Kurniawan, Muhammad Rayhan; Pratiwi, Oktariani Nurul; Hamami, Faqih
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.5910

Abstract

Pendidikan merupakan komponen utama dalam membangun sumber daya manusia yang berkualitas. Ujian merupakan bagian dari proses evaluasi pendidikan untuk mengukur kemampuan siswa dalam memahami materi yang dipelajari. Proses ujian secara online memerlukan fasilitas mengenai pengelolaan soal, sehingga diperlukan klasifikasi untuk mengelompokkan soal sesuai dengan topiknya. Klasifikasi multi-label adalah proses pengelompokan data ke dalam beberapa kelas berdasarkan kesamaan ciri atau karakteristik data, di mana setiap soal dapat memiliki lebih dari satu topik. Penelitian ini berfokus pada pengklasifikasian soal mata pelajaran Bahasa Indonesia tingkat SMP dengan menggunakan metode Problem Transformation dan algoritma Random Forest serta K-Nearest Neighbor (K-NN). Metode Problem Transformation yang digunakan yaitu Binary Relevance, Classifier Chain, dan Label Powerset. Metrik evaluasi untuk menentukan kinerja terbaik yaitu berdasarkan F1-Score dengan K-Fold Cross Validation. Hasil penelitian menunjukkan bahwa algoritma Random Forest memberikan kinerja terbaik dibandingkan K-NN dengan nilai F1-Score terbaik di semua metode Problem Transformation. Nilai F1-Score terbaik dengan metode Label Powerset pada algoritma Random Forest sebesar 69%, dan K-NN sebesar 44%. Berdasarkan hasil tersebut, algoritma Random Forest dengan Label Powerset lebih efektif dalam mengklasifikasikan soal multi-label. Diharapkan penelitian ini dapat memberikan kontribusi dalam meningkatkan efisiensi pengelolaan soal ujian pada sistem pembelajaran online seperti Learning Management System (LMS).
COMPARISON ACCURACY OF C4.5 ALGORITHM AND K-NEAREST NEIGHBORS FOR RAINFALL CLASSIFICATION Muhammad Fauzan Nasrullah; RD. Rohmat Saedudin; Faqih Hamami
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 1 No. 2 (2024): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.14715070

Abstract

Indonesia has a predominantly tropical climate, hence Indonesia experiences limited temperature variations, but has diverse rainfall variations. The variability of rainfall is also inseparable from the impact it has on various aspects of human life and business activities. Therefore, rainfall information is an important aspect in decision making. However, of course, there are stages and methods needed to carry out the analysis process. Therefore, this study looked for the best method between C4.5 and K-Nearest Neighbors which included algorithms in data mining to classify rainfall data. Both algorithms are used to build classification models based on relevant attribute attributes. Then, testing and evaluating both models using various metrics such as Accuracy, Precision, Recall and F1-Score were carried out. In this study also applied Hyperparameter Tuning with the RandomizeSearchCV method to get the best parameters to get maximum accuracy values. The results showed good accuracy values for both algorithms, in the sense that both algorithms were able to classify rainfall based on Indonesia's climate well. Based on the accuracy values obtained with the default parameters of both algorithms, C4.5 produces a higher accuracy value of 81.42%, while K-Nearest Neighbors is only 78.10%. However, after using the best parameters resulting from the application of RandomizedSearchCV Hyperparameter Tuning, a significant change in accuracy value occurred in K-Nearest Neighbors which was found to be 83.37%, while C4.5 increased to 82.56%.
COMPARISON ANALYSIS OF RANDOM FOREST AND NAÏVE BAYES ALGORITHMS FORRAINFALL CLASSIFICATION BASED ON CLIMATE IN INDONESIA Nicolaus Advendea Prakoso Indaryono; RD. Rohmat Saedudin; Faqih Hamami
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 1 No. 2 (2024): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.14715081

Abstract

Indonesia predominantly features a tropical climate across its entirety. With this mostly tropical climate, the country encounters minimal shifts in temperature but exhibits a wide array of rainfall variations. Rainfall patterns in Indonesia showcase significant diversity. These variations in rainfall hold substantial importance in mitigating risks linked to heavy rainfall, such as floods and landslides. Moreover, besides its role in disaster preparedness, rainfall data also holds practical value in sectors such as agriculture, transportation, and industry. By incorporating data mining classification techniques, the process of predicting rainfall in Indonesia can be greatly enhanced. In this study, daily climate data from Indonesia is harnessed, and the chosen method for classification is the random forest algorithm. This selection stems from its capability to generate accurate and consistent classification models without necessitating intricate adjustments of parameters. Furthermore, the Naïve Bayes method is also integrated due to its straightforward implementation and its capacity for simple probability modeling, which can be effectively applied across diverse classification data. The outcomes of this investigation suggest that the random forest algorithm surpasses the Naïve Bayes algorithm in terms of performance and accuracy when classifying climate datasets unique to Indonesia. The random forest algorithm attains an accuracy rate of 86.55%, whereas the Naïve Bayes algorithm lags at an accuracy rate of 36.61%. It is anticipated that these research findings can serve as a point of reference for subsequent scholarly inquiries and contribute to the ongoing monitoring of daily rainfall in Indonesia, thereby aiding in the prevention of natural disasters.
Strategi Manajemen Kapasitas untuk Menangani Traffic Load dengan Algoritma Dijkstra pada PT XYZ Rahmah, Najma Syarifa; Hamami, Faqih; Fa’rifah, Riska Yanu
Jurnal Teknologi Informasi dan Pendidikan Vol. 18 No. 1 (2025): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v18i1.886

Abstract

In the digital era marked by rapid data growth, network congestion occurs when network capacity cannot handle the transmitted data volume, becoming a significant challenge that impacts the Quality of Service (QoS). This study proposes an effective capacity management strategy, which includes alternative routing, load balancing, and capacity enhancement, involving the optimized Dijkstra Algorithm and a Decision Support System (DSS) to improve QoS. The Dijkstra Algorithm is effective in reducing the load on congested routes and ensuring balanced traffic distribution, which enhances the capacity and reliability of the network. The results indicate a reduction in packet loss from 142.353 to 66.340 packets (or 53,39%) and an increase in the number of packets successfully transmitted from 122.542 to 198.555 packets (or 61,98%). Specific node capacity increases have significantly bolstered data transmission success. This comprehensive strategy not only improves the reliability of data transmission and network performance consistency but also enables the network to adapt to growing demands without degrading performance, confirming the efficacy of capacity management in addressing capacity challenges and improving QoS.
Implementasi Data Mining Untuk Product Bundling Pada Coffee Shop Aziz, Abdurrahman; Hamami, Faqih; Yulizar, Iqbal
eProceedings of Engineering Vol. 12 No. 1 (2025): Februari 2025
Publisher : eProceedings of Engineering

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Abstract

Abstrak— Di era sekarang dalam menjalankan bisniscoffee shop sudah terbantu dengan layanan pengantaranmakanan dan minuman, seperti Gojek dan Grab. Dalammenjalankan bisnis coffee shop perlu merancang strategi agarpenjualan produk meningkat. Strategi pemasaran yang umumdilakukan yaitu menggunakan product bundle. Productbundle dilakukan dengan menjual dua produk atau lebihdalam 1 paket, biasanya disertai dengan potongan harga.Dalam penelitian ini, penulis melakukan data mining padadata penjualan milik Authen Café & Space untukmenghasilkan product bundle. Penelitian ini menggabungkanclustering dan association rules mining dalam menghasilkanproduct bundle. Hasil dari proses clustering diperoleh clusterefektif sebanyak tiga cluster. Setiap cluster kemudiandilakukan association rules mining. Association rules miningmenggunakan algoritma fp-growth dengan lift ratio minimalbernilai 1 pada dataset cluster pertama menghasilkan 6 rules,dataset cluster kedua menghasilkan 4 rules dan dataset clusterterakhir menghasilkan 8 rules. Berdasarkan hasil associationrules mining maka saran product bundle yang dapatditerapkan dengan mengambil nilai confidence dan lifttertinggi pada hasil association rules mining setiap cluster. Kata kunci — Product bundle, Clustering, Associationrules, K-means, Fp-growth
Klasifikasi Multi-Label Pada Soal Berdasarkan Kategori Topik Menggunakan Metode Support Vector Machine Novanza, Alvin Renaldy; Pratiwi , Oktariani Nurul; Hamami , Faqih
eProceedings of Engineering Vol. 12 No. 1 (2025): Februari 2025
Publisher : eProceedings of Engineering

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

Abstract

Abstrak — Pendidikan, sebagai bagian penting dalamkehidupan manusia, senantiasa mengalami perkembanganseiring dengan adanya kemajuan ilmu pengetahuan danteknologi (IPTEK). Salah satu inovasi penting dalam pendidikanadalah e-learning, yang memungkinkan siswa belajar tanpaterikat ruang kelas. Namun, dengan semakin banyaknya soalkuis yang beragam topiknya, terutama dalam mata pelajaranIPA yang mencakup berbagai konsep ilmiah, pengelolaan soalsecara manual menjadi tidak efisien. Oleh karena itu,diperlukan sistem klasifikasi yang dapat mengorganisasi danmengelompokkan soal secara otomatis dan efisien, sehingga bisameningkatkan pemahaman siswa, khususnya pada matapelajaran IPA. Penelitian ini memiliki tujuan untukmengimplementasikan algoritma Support Vector Machinedalam proses klasifikasi soal multi-label pada mata pelajaranIPA tingkat SMP. Proses klasifikasi mencakup pembersihandata, case folding, tokenisasi, stopword removal, stemming, danpembobotan atau ekstraksi fitur teks menggunakan TF-IDF.Pemodelan menggunakan pendekatan problem transformationdengan metode label powerset untuk mengubah soal denganmulti-label menjadi bentuk multi-class sehingga bisa dilakukanklasifikasi biner oleh SVM. Evaluasi model dilakukanmenggunakan confusion matrix untuk menganalisis performaklasifikasi dan K-Fold Cross Validation untuk memastikankeakuratan dan generalisasi model. Hasil penelitianmenunjukkan bahwa SVM dapat diterapkan untuk klasifikasisoal multi-label dengan akurasi 67%, serta presisi, recall, danF1-score masing-masing sebesar 75%. Analisis confusion matrixmengungkapkan bahwa model memiliki beberapa kesalahanklasifikasi, mengindikasikan ruang untuk perbaikan lebihlanjut. Meskipun demikian, model SVM menunjukkan potensiyang baik. Penelitian ini juga mengidentifikasi beberapa areauntuk perbaikan, termasuk peningkatan kualitas data danpemilihan parameter model yang lebih optimal. Oleh karena itu,metode SVM layak dipertimbangkan dalam sistem pendidikanuntuk pengembangan bank soal dan sistem evaluasi berbasisteknologi, meskipun diperlukan perbaikan lebih lanjut pada model dan data. Kata kunci— bank soal, confusion matrix e-learning, klasifikasi multi-label, K-Fold Cross Validation, Support VectorMachine.
DETEKSI OBJEK ASET RUMAH SAKIT MENGGUNAKAN COMPUTER VISION DENGAN METODE GENERATIVE ADVERSARIAL NETWORKS Sinung Suakanto; Muhammad Fahmi Hidayat; Faqih Hamami; Anis Farihan Mat Raffei; Edi Nuryatno
JURNAL INFOTEL Vol 17 No 1 (2025): February 2025
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i1.1277

Abstract

Hospital asset monitoring systems encounter significant challenges in managing partially occluded medical equipment, which affects inventory management and operational efficiency. Conventional object detection methods have shown limitations in accurately detecting occluded medical equipment, potentially leading to asset management inefficiencies. This study presents an integrated framework that combines Generative Adversarial Networks (GAN) inpainting with YOLOv8 to improve the detection accuracy of partially occluded medical equipment. The proposed system was evaluated using three distinct training configurations of 500, 750, and 1000 epochs on a comprehensive medical equipment dataset. The experimental results indicate that the 1000-epoch GAN model demonstrated superior reconstruction performance, achieving a Peak Signal-to-Noise Ratio (PSNR) of 39.68 dB, Structural Similarity Index Measure (SSIM) of 0.9910, and Mean Squared Error (MSE) of 7.0030. Furthermore, the integrated YOLOv8-GAN framework maintained robust detection performance with an F1-score of 0.933, comparable to the 0.938 achieved with unoccluded original images. The detection confidence scores exhibited improvement at higher epochs, ranging from 0.824 to 0.861, suggesting enhanced performance with extended training duration. The findings demonstrate that the integration of GAN inpainting with YOLOv8 effectively enhances occluded object detection in hospital environments, offering a viable solution for improved asset monitoring systems.
Generative Adversarial Networks In Object Detection: A Systematic Literature Review Mat Raffei, Anis Farihan; Suakanto, Sinung; Hamami, Faqih; Ismail, Mohd Arfian; Ernawan, Ferda
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.v10i1.1576

Abstract

The intersection of Generative Adversarial Networks (GANs) and object detection represents one of the most promising developments in modern computer vision, offering innovative solutions to longstanding challenges in visual recognition systems. This review presents a systematic analysis of how GANs are transforming these challenges, examining their applications from 2020 to 2025. The paper investigates three primary domains where GANs have demonstrated remarkable potential: data augmentation for addressing data scarcity, occlusion handling techniques designed to manage visually obstructed objects, and enhancement methods specifically focused on improving small object detection performance. Analysis reveals significant performance improvements resulting from these GAN applications: data augmentation methods consistently boost detection metrics such as mAP and F1-score on scarce datasets, occlusion handling techniques successfully reconstruct hidden features with high PSNR and SSIM values, and small object detection techniques increase detection accuracy by up to 10% Average Precision in some studies. Collectively, these findings demonstrate how GANs, integrated with modern detectors, are greatly advancing object detection capabilities. Despite this progress, persistent challenges including computational cost and training stability remain. By critically analyzing these advancements and limitations, this paper provides crucial insights into the current state and potential future developments of GAN-based object detection systems.
Klasifikasi Kualitas Udara Pada Provinsi Dki Jakarta Menggunakan Alogritma Random Forest Al amudi, Farhan Hasan; Hamami , Faqih; Almaarif, Ahmad
eProceedings of Engineering Vol. 12 No. 2 (2025): April 2025
Publisher : eProceedings of Engineering

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Abstract

Udara merupakan salah satu unsur penting bagi lingkungan dan menjadi lebutuhan utama untuk menopang kehidupan makhluk hidup. Proses metabolisme pada makhluk hidup tidak mungkin berlangsung tanpa oksigen yang diambil dari udara. Selain oksigen, udara juga mengandung zat lain seoerti karbon monoksida, kabron diaksida, formaldehhida, jamur, virus, dan sebagainya. Zat-zat tersebut masih dapat dinetralisir selama berada dalam batas aman, namu ketika melebihi ambang batas, proses netralisasi akan terganggu. Peningkatan kandungan zat-zat tersebut di udara umumnya diakibatkan oleh aktivitas manusia. Di tahun 2019 indonesia mencapai titik pencemaran udara terburuk yang sudah mencapai titik merah yang menandakan tidak sehat nya udara yang ada pada DKI Jakarta. Salah satu cara untuk memantau informasi mengenai kualitas udara adalah dengan menggunakan metode klasifikasi. Pada penelitian ini klasifikasi dilakukan menggunakan dataset ISPU pencemaran udara Provinsi DKI Jakarta dari tahun 2019 sampai tahun 2022. Klasifikasi yang tepat dapat sangat membantu pemerintah dalam merumuskan kebijakan. Kebijakan ini bertujuan untuk mengendalikan polusi agar sesuai dengan standar kualitas udara yang bermanfaat bagi kelangsungan hidup makhluk hidup. Dalam penelitian ini, model klasifikasi yang digunakan adalah random forest, dengan lima atribut yaitu PM10, SO2,NO, O3, dan CO2, serta kategori sebagai target label. Kata kunci— Udara, DKI Jakarta, ISPU, Klasifikasi, Random Forest
Co-Authors Agus Maolana Hidayat Ahmad, Mokhtarrudin Al amudi, Farhan Hasan Aldi Akbar Anis Farihan Mat Raffei Anis Farihan Mat Raffei Aprilia Mega Puspitasari Arrahmani, Farras Hilmy Aziz, Abdurrahman Brillian Adhiyaksa Kuswandi Budi Rustandi Kartawinata Dahlan, Iqbal Ahmad Deandra, Valen Deden Witarsyah Dimas Raihan Zein Dina Meliana Saragi Edi Nuryatno Fa'rifah, Riska Yanu Fadhil Hidayat Faishal Mufied Al Anshary Febrianti, Ferda Ayu Dwi Putri Ferda Ayu Dwi Putri Febrianti Ferda Ernawan Fetty Fitriyanti Lubis Firzania, Heidea Yulia Fitri Bimantoro Hadwirianto, Muhammad Raihan Helmayanti, Sheva Aditya I Gede Pasek Suta Wijaya Ilma Nur Hidayati Iqbal Ahmad Dahlan Iqbal Santosa Irfan Darmawan Ismail, Mohd Arfian Jauhari, M.Habib Jody Mardika Joel Rayapoh Damanik Khairunnisa Salsabila Riswanti Kurniawan, Muhammad Rayhan Lubis, Rizki Aulia Akbar Mangsor, Miza Mat Raffei, Anis Farihan Muhammad Azzam Imaduddin Muhammad Bryan Gutomo Putra Muhammad Fahmi Hidayat Muhammad Fauzan Nasrullah Muhammad Hafizh Murahartawaty Murahartawaty Nasrullah, Muhammad Fauzan Nicolaus Advendea Prakoso Indaryono Novanza, Alvin Renaldy Nuraliza, Hilda Nurul Hidayati Oktariani Nurul Pratiwi Orvalamarva Pratiwi, Oktaria Nurul Puruhita, Maretha Fitrie Rachmadita Andreswari Rahmah, Najma Syarifa Rahmat Fauzi Ramdani, Dwi Fickri Insan Razali, Raja Razana Raja Rd. Rohmat Saedudin Ruth Sesilya Ambarita Satya Nugraha, Gibran Sheva Aditya Helmayanti Silmy Sephia Nurashila Sinung Suakanto Suhono Harso Supangkat Sujak, Aznul Fazrin bin Abu Syfani Alya Fauziyyah Tatang Mulyana Tien Fabrianti Kusumasari Vina Fadillah Widyadhari, Dinda Putri Yudo Husodo, Ario Yulizar, Iqbal Yuni Kardila Zahid, Azham