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All Journal JUSIFO : Jurnal Sistem Informasi 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 IJIES (International Journal of Innovation in Enterprise System) 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 Proceeding of Community Service and Engagement
Faqih Hamami
Department Of Information System, Telkom University
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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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.
PELATIHAN PEMBUATAN DAN MENAJEMEN BLOG INSPIRATIF BAGI SISWA MENENGAH BINAAN YAYASAN GRIYA SODAQO INDONESIA Faqih Hamami; Faishal Mufied Al Anshary; Nur Ichsan Utama
The Proceeding of Community Service and Engagement (COSECANT) Seminar Vol. 3 No. 1 (2023): Prosiding COSECANT : Community Service and Engagement Seminar
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/cosecant.v3i1.7134

Abstract

Yayasan Griya Sodaqo Indonesia merupakan lembaga amal yang mengutamakan pemberdayaan anak-anak yatim piatu di Jawa Barat dalam berbagai bidang, termasuk dalam bidang pendidikan. Meskipun yayasan ini telah memberikan bantuan pendidikan, keberlanjutan dan pemahaman teknologi masih menjadi aspek yang perlu diperkuat. Saat ini, belum terdapat substansi pembelajaran yang mendorong peningkatan literasi teknologi bagi anak-anak binaan Yayasan Griya Sodaqo. Sebagai respons terhadap kebutuhan ini, Dosen Sistem Informasi Fakultas Rekayasa Industri Universitas Telkom menginisiasi sebuah pelatihan yang bertujuan untuk meningkatkan pemahaman IPTEK anak-anak yatim piatu tersebut. Pelatihan ini difokuskan pada Pembuatan dan Manajemen Blog Inspiratif menggunakan platform WordPress. Dengan mengadopsi pendekatan praktis dan kreatif, pelatihan ini dirancang untuk memberikan pemahaman yang mendalam tentang penggunaan teknologi blog sebagai sarana ekspresi diri dan peningkatan literasi digital
PELATIHAN DESIGN THINKING UNTUK INOVASI BISNIS BAGI REMAJA DI PANTI YAYASAN GRIYA SODAQO INDONESIA. Faqih Hamami; Faishal Mufied Al Anshary; Muhammad Iqbal; Hanif Azhar
The Proceeding of Community Service and Engagement (COSECANT) Seminar Vol. 4 No. 2 (2024): The Proceeding of Community Service and Engagement (COSECANT) Seminar
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/cosecant.v4i2.8497

Abstract

Design thinking merupakan satu pendekatan dalam inovasi bisnis yang banyak diimplementasikan di berbagai sektor. Kompetensi design thinking masih jarang dibahas di berbagai forum khususnya di sekolah menengah atas. Hal ini bisa disebabkan kurangnya sumber daya dan kurikulum yang baik. Inovasi bisnis merupakan langkah penting agar bisa tetap bertahan di tengah gempuran kompetisi. Pendidikan inovasi bisnis perlu menjadi bagian dari pembelajaran siswa sebagai bentuk keterampilan dasar dalam menyongsong masa depan. Sebagai respon kebutuhan Pendidikan inovasi, Dosen Telkom University melalui skema Pengabdian kepada Masyarakat (PkM) mengadakan pelatihan design thinking untuk meningkatkan pemahaman pelajar. Kolaborasi Universitas Telkom dengan Yayasan Griya Sodaqo Indonesia, yang merupakan yayasan amal dengan berbagai rekanan lembaga yatim dan sekolah dari berbagai jenjang. Pelatihan design thinking dilaksanakan di ruang active class untuk mendukung pembelajaran yang kreatif dan kolaboratif.
Penggalian Potensi Kewirausahaan Anak Panti Asuhan Melalui Pendekatan Design Thinking Faqih Hamami; Faishal Mufied Al Anshary; Dita Pramesti
The Proceeding of Community Service and Engagement (COSECANT) Seminar Vol. 5 No. 1 (2025): The Proceeding of Community Service and Engagement (COSECANT) Seminar
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/cosecant.v5i1.9331

Abstract

Kewirausahaan atau dikenal dengan entrepreneurship adalah kemampuan penting yang harus dimiliki pelajar. Dengan memiliki ilmu dasar kewirausahaan diharapkan anak dapat dengan mudah mengidentifikasi kondisi sekitar dan membangun solusi nyata. Salah satu pendekatan yang umum digunakan adalah design thinking yang berorientasi pada kebutuhan manusia. Sebagai bentuk kontribusi dalam penguatan keterampilan kewirausahaan, dosen Universitas Telkom berkolaborasi dengan Yayasan Griya Sodaqo Indonesia melalui program Pengabdian kepada Masyarakat (PkM) mengadakan pelatihan design thinking untuk menggali potensi anak panti asuhan. Kegiatan ini dirancang untuk mendorong kemampuan berpikir anak untuk berfikir aktif dan kreatif terhadap kondisi di lingkungan mereka. Pelatihan dilaksanakan di ruang kelas yang telah didesain agar mendukung proses pelatihan dengan baik. Berdasarkan hasil feedback pelatihan diperoleh informasi bahwa semua peserta memberikan nilai setuju dan sangat setuju untuk semua komponen. tiga komponen penilaian mendapatkan lebih dari 50% dan komponen tiga mendapatkan persentase tertinggi bahwa 84.2% peserta sangat setuju untuk keramahan dan ketanggapan Dosen dan mahasiswa selama pelatihan kewirausahaan.
Implementasi GenAI dalam Sistem Pembuatan Butir Soal Otomatis dengan Pendekatan Microservices Herdian, Hafiizh; Pratiwi, Oktariani Nurul; Hamami, Faqih
eProceedings of Engineering Vol. 10 No. 1 (2023): Februari 2023
Publisher : eProceedings of Engineering

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

Abstract

Pembuatan soal yang relevan, bervariasi, dan sesuai dengan kurikulum merupakan tantangan besar bagi tenaga pengajar, terutama dengan keterbatasan waktu dan beban kerja administratif. Penelitian ini bertujuan mengembangkan sistem pembuatan butir soal otomatis berbasis Generative Artificial Intelligence (GenAI) dengan model Gemini 1.5 Flash dan pendekatan arsitektur microservices. Sistem menghasilkan soal pilihan ganda dan isian singkat dari input materi berupa teks, PDF, atau dokumen. Pengembangan dilakukan menggunakan metodologi Extreme Programming (XP) dengan implementasi teknologi Next.js, TypeScript, PostgreSQL, Docker, dan RabbitMQ. Hasil menunjukkan sistem mampu menghasilkan 10 soal dengan waktu rata-rata 8,5 detik dan menyediakan layanan modular, skalabel, serta responsif terhadap perubahan kebutuhan pengguna. Validasi dilakukan uji fungsional dan observasi pengguna (user acceptence test), serta menunjukkan efektivitas dan efisiensi sistem dalam konteks pendidikan digital. Kata kunci— generative AI, Gemini 1.5 Flash, pembuatan soal otomatis, microsevices, XP
Implementasi Logika Fuzzy Untuk Pendukung Keputusan Sistem Penyiraman Otomatis Tanaman Anthurium Dina Meliana Saragi; Faqih Hamami; Tatang Mulyana
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 1 (2022): September 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4895

Abstract

Anthurium is a class of ornamental plants that are admired by many lovers of ornamental plants, this plant is cultivated on a wide scale in the floriculture industry. There are factors that support the current high price of anthurium plants, first, a unique species with a ratio of 10% of anthurium seeds that grow exactly the same as the parent. In addition, anthurium growth is very slow and difficult to care for. Other factors must be considered in the cultivation of anthurium plants, namely air temperature, humidity, sunlight, acidity (pH) and water requirements. This anthurium plant is a plant that is sensitive to water so it requires supervision of regular watering so that the plant does not die. Farmers need advanced expert knowledge in making different decisions related to agriculture, especially in dosing and timing of crop watering. Therefore, in this study, researchers designed fuzzy logic according to the needs of anthurium plants with a rule base that can change IoT sensor data in the form of DHT11 sensors and Soil Moisture Sensors FC-28 into the output of a decision on the duration of plant watering. In this stage, the process of fuzzification, inference and defuzzification. The results obtained during this research are comparative testing of 15 values from the output devices that are taken at random approximately closer to the values from the simulation with MATLAB with a total difference of 8.61% due to the difference in calculations between IoT devices and simulations with MATLAB, but this can still be categorized accurately because the output results of the MATLAB tool and simulation are still within the range of membership function values.
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).
Penerapan Algoritma YOLO V11 untuk Deteksi Tingkat Kekondusifan Pembelajaran di Kelas Putri Chichilia Nababan; Nur Ichsan Utama; Faqih Hamami
eProceedings of Engineering Vol. 13 No. 1 (2026): Februari 2026
Publisher : eProceedings of Engineering

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

Abstract

Permasalahan utama dalam proses pembelajarantatap muka adalah kurangnya pemantauan objektif terhadapperilaku mahasiswa di dalam kelas, terutama terkait tingkatfokus selama kegiatan belajar berlangsung. Perilaku tidakfokus seperti tidur dan menggunakan ponsel kerap terjaditanpa dapat terdeteksi secara real-time oleh pengajar, yangberdampak pada penurunan efektivitas pembelajaran. Untukmengatasi hal ini, penelitian ini mengusulkan sistem pendeteksiperilaku mahasiswa berbasis algoritma YOLOv11, yangbertugas mendeteksi tiga jenis gerakan: tidur, menggunakanponsel, dan memperhatikan. Data citra diperoleh melaluidokumentasi video di kelas, kemudian dilabeli secara manualdan dilatih menggunakan model YOLOv11 dengan parameterutama 50 epoch, ukuran gambar 640 piksel, dan batch size 16.Model yang dikembangkan dievaluasi menggunakan metrikevaluasi seperti accuracy, precision, recall, F1-score, dan meanAverage Precision (mAP). Hasil evaluasi menunjukkan nilaiprecision accuracy sebesar 0.9652 sebesar 0.9457, recall sebesar0.9575, F1-score sebesar 0.9515, mAP@50 sebesar 0.9672, danakurasi sistem mencapai 0.9662. Output dari sistem berupa fileExcel (.xlsx) yang memuat timestamp, jenis perilaku yangterdeteksi, dan confidence score, serta proporsi persentaseperilaku fokus dan tidak fokus dalam kelas.Berdasarkan hasil tersebut, dapat disimpulkan bahwaYOLOv11 dapat diterapkan secara efektif dalam mendeteksidan mengklasifikasikan perilaku mahasiswa, serta memberikangambaran yang objektif mengenai tingkat kekondusifanpembelajaran di kelas.kata kunci: YOLOv11, deteksi perilaku, computer vision,fokus belajar, deep learning
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.
Co-Authors Agus Maolana Hidayat Ahmad, Mokhtarrudin Al amudi, Farhan Hasan Aldi Akbar Ambarita, Ruth Sesilya Anis Farihan Mat Raffei Anis Farihan Mat Raffei Arrahmani, Farras Hilmy Aziz, Abdurrahman Azzam Imaduddin, Muhammad Budi Rustandi Kartawinata Dahlan, Iqbal Ahmad Deandra, Valen Deden Witarsyah Dimas Raihan Zein Dina Meliana Saragi Dita Pramesti Edi Nuryatno 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 Hanif Azhar Helmayanti, Sheva Aditya Herdian, Hafiizh Hidayati, Ilma Nur I Gede Pasek Suta Wijaya Iqbal Santosa Irfan Darmawan Ismail, Mohd Arfian Jauhari, M.Habib Joel Rayapoh Damanik Kardila, Yuni Kurniawan, Muhammad Rayhan Kuswandi, Brillian Adhiyaksa Lubis, Rizki Aulia Akbar Luthfi Ramadani Mangsor, Miza Mardika, Jody Mat Raffei, Anis Farihan Maulana, Fakhri Hassan Muhammad Bryan Gutomo Putra Muhammad Fahmi Hidayat Muhammad Fauzan Nasrullah Muhammad Fauzan Nasrullah Muhammad Hafizh Muhammad Iqbal Murahartawaty Murahartawaty Nasrullah, Muhammad Fauzan Nicolaus Advendea Prakoso Indaryono Nicolaus Advendea Prakoso Indaryono Novanza, Alvin Renaldy Nur Ichsan Utama Nuraliza, Hilda Nurul Hidayati Oktariani Nurul Pratiwi Orvalamarva Pratiwi, Oktaria Nurul Puruhita, Maretha Fitrie Puspitasari, Aprilia Mega Putri Chichilia Nababan Rachmadita Andreswari Rahmah, Najma Syarifa Rahmat Fauzi Ramdani, Dwi Fickri Insan Ramli, Muhammad Ayyub Razali, Raja Razana Raja Rd. Rohmat Saedudin Riska Yanu Fa’rifah Salsabila Riswanti, Khairunnisa 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 Widyatasya Agustika Nurtrisha Yudo Husodo, Ario Yulizar, Iqbal Zahid, Azham