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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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Analisis Sentimen Berbasis Aspek Terhadap Ulasan Pengguna Aplikasi Pegadaian Digital Dengan Multiclass Multioutput Menggunakan Algoritma Support Vector Machine Vina Fadillah; Faqih Hamami; Rachmadita Andreswari
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 4, No 4 (2023): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v4i4.248

Abstract

Badan Usaha Milik Negara (BUMN) are one of the three main economic players in the country, alongside cooperatives and private enterprises, aiming to realize a prosperous society in various fields. One of the BUMN, PT Pegadaian, operates in the financial sector. According to the directorate regulation of PT Pegadaian Number 122 of 2020, to enhance the quality and maturity level of information technology (IT), evaluation and monitoring mechanisms based on the international standard ISO/IEC 25010:2011 for System Software Quality Models are required in managing IT quality. Therefore, PT Pegadaian is currently undergoing a transformation process to expand its business model that was originally only feasible through offline means, making it possible to be done online. To support this Pegadaian transformation process, an application named "Pegadaian Digital" has been developed, containing PT Pegadaian's core businesses, such as buying and selling gold savings, pawn booking, and gold price reviews. To assist digital transformation process, sentiment analysis research is conducted based on various aspects to identify aspects in the application that need to be improved and maintained. The study focuses on user reviews from Google Play Store, utilizing the KDD process and Support vector machine algorithm. The aspects used in this research are Learnability, Efficiency, Errors, and Satisfaction, each aspect labelled as positive, negatif, and neutral (not exist). The testing in this research is divided into two scenarios, focusing on the model with default parameter and parameter with hyperparameter tuning. Subsequently, the model is evaluated with accuracy, precision, recall, F1-score, and K-Fold Cross Validation. The evaluation results show that the scenario with a split data ratio of 80:20 using SVM with basic or default parameters gets the best performance results based on an accuracy value of 86%, recall 80%, f1-score 82%, precision 84%, and model did not overfitting
Implementasi Algoritma Klasifikasi Naïve Bayes Untuk Klasifikasi Credit Scoring Pada Platform Peer-To-Peer Lending Joel Rayapoh Damanik; Rahmat Fauzi; Faqih Hamami
Journal of Computer System and Informatics (JoSYC) Vol 4 No 4 (2023): August 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i4.4059

Abstract

Di Indonesia, seiring dengan pertumbuhan fintetch mengenai P2P lending, juga timbul P2P lending ilegal yang beroperasi tanpa izin dari otoritas berwenang. Diperkirakan terdapat sekitar 400 perusahaan fintech ilegal yang beroperasi di Indonesia. Masalah umum yang dihadapi oleh platform P2P lending adalah ketidakmampuannya mengantisipasi pembayaran yang gagal oleh peminjam. Hal ini disebabkan oleh tingginya suku bunga yang diterapkan dan kurangnya seleksi terhadap peminjam dengan risiko kredit yang tinggi atau rendah. Untuk mengatasi masalah ini, diperlukan pengembangan model sistem Machine Learning yang dapat mengklasifikasikan data peminjam dan terintegrasi dengan informasi dari lembaga keuangan lainnya. Tujuannya adalah untuk menyaring calon peminjam dengan risiko kredit yang tinggi atau rendah. Salah satu model yang digunakan adalah algoritma Naïve Bayes Classifier. Metode ini berdasarkan teorema Bayes dan merupakan algoritma yang memproses klasifikasi sederhana dengan independensi variabel. Penggunaan algoritma Naïve Bayes Classifier diharapkan dapat menciptakan sistem yang membantu platform P2P lending dalam seleksi calon peminjam. Model ini akan memprediksi risiko kredit rendah atau tinggi bagi pengguna atau pelanggan P2P lending. Untuk mencapai performa klasifikasi optimal, dilakukan tuning hyperparameter pada setiap simulasi. Hyperparameter tuning adalah proses mencari nilai terbaik untuk parameter dalam model machine learning guna meningkatkan performa. Pada algoritma GaussianNB, parameter yang dituning adalah var_smoothing. Hasil tuning hyperparameter terbaik ditemukan dengan nilai var_smoothing sebesar 0.009638958856642498, dengan pembagian train_size dan test_size sebesar 70:30. Dengan konfigurasi ini, model mencapai tingkat akurasi sebesar 95%.
PENERAPAN ALGORITMA K-MEANS UNTUK MEMETAKAN PERSEBARAN FASILITAS DAN TENAGA KESEHATAN DI KOTA BANDUNG Faqih Hamami; Iqbal Ahmad Dahlan
JRIS : Jurnal Rekayasa Informasi Swadharma Vol 4, No 2 (2024): JURNAL JRIS EDISI JULI 2024
Publisher : Institut Teknologi dan Bisnis (ITB) Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jris.vol4no2.561

Abstract

Good health facilities and personnel are important elements in realizing public health. Several health facilities, such as hospitals, health centers, clinics, and pharmacies, act as a platform for health services. An unbalanced distribution of health facilities and personnel creates disparities in health quality. Population growth in Bandung City is a challenge for the distribution of health facilities. Inequality in each neighborhood has an impact on the difficulty of accessing health services and the quality of those services. Several studies have segmented health facilities separately from health workers. This study aims to map the distribution of health facilities and health workers together in Bandung City using the K-Means algorithm. Segmentation of facilities and health workers in the city of Bandung is done with the stages of data collection, data cleansing, data transformation, and data modeling, and then segmentation using a clustering approach with the K-Means algorithm. The results of the study, using the number k = 3, then formed clusters with low, middle, and high categories. Based on the segmentation results, a cluster was obtained consisting of 21 sub-districts that were lacking in health resources; 8 sub-districts were quite good, and 1 sub-district was abundant.Fasilitas dan tenaga kesehatan yang baik merupakan elemen penting dalam mewujudkan kesehatan masyarakat. Beberapa fasilitas kesehatan seperti rumah sakit, puskesmas, klinik dan apotek berperan sebagai wadah dalam layanan kesehatan. Distribusi fasilitas dan tenaga kesehatan yang tidak berimbang akan menciptakan kesenjangan dan kualitas kesehatan. Pertumbuhan penduduk di Kota Bandung menjadi tantangan untuk distribusi sarana kesehatan. Ketidakmerataan di setiap kecamatan mempunyai dampak kesulitan akses layanan kesehatan dan kualitas layanan yang rendah. Beberapa penelitian yang telah dilakukan banyak melakukan segmentasi fasilitas kesehatan secara terpisah dari tenaga kesehatan. Penelitian ini bertujuan untuk memetakan persebaran fasilitas dan tenaga kesehatan secara bersama-sama di Kota Bandung dengan menggunakan algoritma K-Means. Segmentasi fasilitas dan tenaga kesehatan di kota Bandung dilakukan dengan tahapan data collection, data cleansing, data transformation dan data modeling kemudian dilakukan segmentasi menggunakan pendekatan clustering dengan algoritma K-Means. Hasil penelitian dengan menggunakan jumlah k = 3 maka terbentuk kluster dengan kategori low, middle dan high. Berdasarkan hasil segmentasi diperoleh cluster yang terdiri dari 21 kecamatan yang kurang dalam sumber daya kesehatan, 8 kecamatan cukup baik dan 1 kecamatan yang berlimpah.
PERBANDINGAN AKURASI ALGORITMA C4.5 DAN K-NEAREST NEIGHBORS UNTUK KLASIFIKASI CURAH HUJAN BERDASARKAN IKLIM INDONESIA Nasrullah, Muhammad Fauzan; Saedudin, Rd. Rohmat; Hamami, Faqih
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 2 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i2.4655

Abstract

Indonesia has a dominant tropical climate, which is why it experiences limited temperature variations but diverse rainfall patterns. The variability of rainfall is closely intertwined with the impacts it exerts on various aspects of human life and business activities. Therefore, rainfall information constitutes a crucial aspect in decision-making. However, of course, there is a need for stages and methods to conduct the analysis process. Hence, this study aims to determine the superior method between C4.5 and K-Nearest Neighbors, both of which are algorithms in data mining, for classifying rainfall data. Both algorithms are employed to construct classification models based on relevant attributes. Subsequently, these models are tested and evaluated using various metrics such as Accuracy, Precision, Recall, and F1-Score. In this study, Hyperparameter Tuning is also applied using the RandomizedSearchCV method to obtain optimal parameters that can yield maximum accuracy. The research findings indicate that both algorithms perform well in rainfall classification. When considering the accuracy values obtained with the default parameters of both algorithms, C4.5 exhibits a higher accuracy rate of 81.42%, whereas K-Nearest Neighbors only achieves 78.10%. However, after utilizing the best parameters resulting from the implementation of Hyperparameter Tuning with RandomizedSearchCV, a significant accuracy improvement is observed in K-Nearest Neighbors, which reaches 83.37%. Meanwhile, C4.5's accuracy increases to 82.56%.
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.
Counseling on Cooperative Management as a Driving Force for Economic Empowerment at the Sukamiskin Islamic Boarding School Bandung Kartawinata, Budi Rustandi; Akbar, Aldi; Hidayat, Agus Maolana; Witarsyah, Deden; Hamami, Faqih; Pratiwi, Oktaria Nurul; Ahmad, Mokhtarrudin; Zahid, Azham; Sujak, Aznul Fazrin bin Abu; Razali, Raja Razana Raja; Mangsor, Miza
Journal of Community Service and Society Empowerment Том 2 № 02 (2024): Journal of Community Service and Society Empowerment
Publisher : PT. Riset Press International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59653/jcsse.v2i02.708

Abstract

This Community Service aims to provide understanding to cooperative administrators and students at the Sukamiskin Islamic Boarding School regarding the benefits and effective management strategies of cooperatives. The method used in this research is interviews and direct observation of cooperative administrators and students involved in cooperative activities. The research results show that counseling about cooperative management is very important to increase the understanding and skills of cooperative administrators and students in managing cooperatives. In this outreach, information and training is provided regarding the basic principles of cooperative management, financial planning, risk management and marketing of cooperative products. With this outreach, it is hoped that cooperative management at the Sukamiskin Islamic Boarding School can become more effective and have a positive impact on community economic empowerment. Cooperative administrators and students will become more competent in managing cooperatives, so that they can generate better income for their members. Counseling on cooperative management as a driving force for economic empowerment at the Sukamiskin Islamic Boarding School, Bandung City is very important to increase the understanding and skills of cooperative administrators and students in managing cooperatives. It is hoped that this counseling can provide significant benefits in increasing the income and welfare of cooperative members.
WORKSHOP PEMROGRAMAN SCRATCH UNTUK YATIM & PIATU YAYASAN GRIYA SODAQO INDONESIA Al Anshary, Faishal Mufied; Fauzi, Rahmat; Hamami, Faqih
Prosiding Konferensi Nasional Pengabdian Kepada Masyarakat dan Corporate Social Responsibility (PKM-CSR) Vol 6 (2023): INOVASI PERGURUAN TINGGI & PERAN DUNIA INDUSTRI DALAM PENGUATAN EKOSISTEM DIGITAL & EK
Publisher : Asosiasi Sinergi Pengabdi dan Pemberdaya Indonesia (ASPPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37695/pkmcsr.v6i0.1961

Abstract

Griya Sodaqo Indonesia Foundation is a charity organization that focuses on orphans and children especially in the city of Bandung. However, these children often do not have the opportunity to learn programming and technology, which are very important skills in today's digital age. As an effort to provide opportunities to learn programming for children at the Griya Sodaqo Foundation. Information Systems Lecturer at the Faculty of Industrial Engineering, Telkom University, organizes a Visual Scratch Programming Workshop in an easy and easy-to-understand way. With this workshop, children learn about programming concepts in a creative and interactive way. Through this workshop, orphans at Griya Sodaqo get relevant skill opportunities and become confident in facing future technological challenges. The results of this workshop activity 96% of participants were very enthusiastic about this agenda by providing feedback, they strongly agreed with the implementation of basic programming training with Scratch.
PENERAPAN ALGORITMA TF-IDF DAN NAÏVE BAYES UNTUK ANALISIS SENTIMEN BERBASIS ASPEK ULASAN APLIKASI FLIP PADA GOOGLE PLAY STORE Helmayanti, Sheva Aditya; Hamami, Faqih; Fa’rifah, Riska Yanu
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 3 (2023): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i3.415

Abstract

The development of the internet has changed people's lifestyle with the existence of FinTech. One of the popular FinTech innovations is the Flip digital wallet application. In this study, aspect-based sentiment analysis was carried out on Flip user reviews using the naive bayes algorithm. The test results show high accuracy, with an average accuracy of 0.84. The naive bayes algorithm is effective in classifying user reviews based on aspects of speed, security, and cost, with accuracies of 0.80, 0.87, and 0.84, respectively. This research provides important insights for service providers to improve service performance and innovation. The labelling data generated the most sentiment 0 (no sentiment), followed by sentiment 1 (positive) and 2 (negative). Negative sentiments have a high frequency on speed and security aspects, while positive sentiments have a high frequency on cost aspects. Thus, improvements are needed to the security system and speed of the Flip application to increase user satisfaction in these aspects. The naive bayes algorithm can be a useful tool in processing review data on e-wallet applications and similar services.
ASPECT-BASED SENTIMENT ANALYSIS TERHADAP ULASAN APLIKASI FLIP MENGGUNAKAN PEMBOBOTAN TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF) DENGAN METODE KLASIFIKASI K-NEAREST NEIGHBORS (K-NN) Febrianti, Ferda Ayu Dwi Putri; Hamami, Faqih; Fa’rifah, Riska Yanu
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 3 (2023): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i3.429

Abstract

The rapid growth of online transactions in Indonesia has increased the demand for efficient interbank transfer solutions. However, the costs associated with such transactions have become a significant obstacle. Flip, a company with a vision to become a global leader in customer satisfaction-driven services, offers a solution to this challenge. This study proposes an aspect-based sentiment analysis method using the K-Nearest Neighbors (K-NN) algorithm to analyze user sentiment on key aspects, namely speed, security, and the cost of using the Flip application. The results of this research provide valuable information that can be used as a basis to provide insights, suggestions and recommendations to businesses, so they can create better solutions and promote optimal user experience. The research results show that the K-NN model has the ability to predict user psychology well in all aspects, with a significant level of accuracy, specifically speed (73.04%), security (86, 05%) and costs (80.11%). In addition, this study also compares two model validation methods: simple data splitting method and K-Fold cross-validation. Although the simple data splitting method has a higher average accuracy, K-fold cross-validation is considered superior as it provides a more accurate and reliable estimate of the overall performance of the model. Sentiment analysis results show that Flip app users tend to give negative feedback on speed and security, while they give positive feedback on cost. Therefore, the main recommendation is that the company PT Fliptech Lentera Inspirasi Pertiwi improves the speed and security aspects to increase user satisfaction with the Flip application. Therefore, this customer-centric service will continue to prioritize user satisfaction as its primary goal.
Arabic Character Recognition Using CNN LeNet-5 Satya Nugraha, Gibran; Suta Wijaya, I Gede Pasek; Bimantoro, Fitri; Yudo Husodo, Ario; Hamami, Faqih
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2422

Abstract

The human handwriting pattern is one of the research areas of pattern recognition; it is very complex. Therefore, research in this field has become quite popular. Moreover, human handwriting pattern recognition is needed for several things, one of them being character recognition. Recognition of Arabic handwriting is complex because everyone has different characteristics in writing and Arabic characters have quite abstract shapes and patterns. From previous research, Convolutional Neural Network (CNN), a deep learning-based algorithm, has a fairly high accuracy value when used for public datasets such as AHDB and private datasets. In this study, private datasets are used with a fairly high level of complexity because the respondents appointed to write Arabic letters come from different age categories. The CNN architecture used in this research is the architecture developed by Yan LeCun known as LeNet-5. The local dataset used was 8400 images, with details of 6720 for training data (each letter has 240 images) and 1680 for testing data (each letter has 60 images). The total respondents who wrote Arabic script were 30 people, and each person wrote each letter ten times. The accuracy obtained is 81% higher than in previous studies. The following study will test a number of additional CNN architectures to increase the accuracy of the results. In addition to accuracy, this study will also calculate the misclassification rate, root mean square error, and mean absolute error.
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