Article Per Year (5 Year)
p-Index From 2020 - 2025
1.829
P-Index
This Author published in this journals
All Journal Indonesian Journal of Mathematics and Natural Sciences Journal of Engineering and Technological Sciences Scientific Journal of Informatics JITK (Jurnal Ilmu Pengetahuan dan Komputer) SANITAS: Jurnal Teknologi dan Seni Kesehatan Journal of Soft Computing Exploration INTERNATIONAL JOURNAL OF CULTURAL AND SOCIAL SCIENCE Journal of Advances in Information Systems and Technology
Hakim, M. Faris Al
Department Of Islamic Economic And Business, State Islamic University Of North Sumatera
Humanities Computer Science & IT Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 10 Documents
Search

 1 
Analisis Perbandingan Akurasi Metode Fuzzy Tsukamoto dan Fuzzy Sugeno Dalam Prediksi Penentuan Harga Mobil Bekas Reynaldi, Reynaldi; Syafrizal, Wahyu; Hakim, M. Faris Al
Indonesian Journal of Mathematics and Natural Sciences Vol 44, No 2 (2021): October 2021
Publisher : Universitas Negeri Semarang

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

Abstract

Mobil merupakan transportasi darat yang sangat membantu aktivitas manusia dalam melakukan kegiatan seharihari. Toyota Avanza 47% mendominasi pasar mobil bekas dibanding merek lainnya. Dalam transaksi jual beli mobil bekas, selisih harga yang berbeda sering memiliki nilai yang jauh berbeda. Logika fuzzy dapat digunakan untuk memprediksi harga mobil bekas dengan memperhatikan beberapa aspek. Penelitian ini bertujuan untuk membandingkan tingkat akurasi prediksi antara metode Fuzzy Tsukamoto dengan Fuzzy Sugeno. Fuzzy Tsukamoto bersifat intuitif dan dapat memberikan tanggapan berdasarkan informasi yang bersifat kualitatif, tidak akurat, dan ambigu. Sedangkan Fuzzy Sugeno yang terdiri atas basis aturan dengan beberapa aturan penarikan kesimpulan fuzzy. Gagasan ini ditulis dengan analisis melalui studi literatur buku, jurnal dan pengumpulan data berupa dataset maupun landasan teori yang terkait. Berdasarkan analisis data sampel penjualan mobil bekas dan pembandingan 2 metode dengan variabel yang sama. Hasil dari penelitian yang telah dihitung, diperoleh bahwa metode Fuzzy Tsukamoto memiliki tingkat error sebesar 8% dan Fuzzy Sugeno sebesar 38% pada prediksi harga mobil Toyota Avanza bekas.
Prediction of COVID-19 Using Recurrent Neural Network Model Alamsyah, Alamsyah; Prasetiyo, Budi; Hakim, M. Faris Al; Pradana, Fadli Dony
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i1.30070

Abstract

Purpose: The COVID-19 case that infected humans was first discovered in China at the end of 2019. Since then, COVID-19 has spread to almost all countries in the world. To overcome this problem, it takes a quick effort to identify humans infected with COVID-19 more quickly. Methods: In this paper, RNN is implemented using the Elman network and applied to the COVID-19 dataset from Kaggle. The dataset consists of 70% training data and 30% test data. The learning parameters used were the maximum epoch, learning late, and hidden nodes. Result: The research results show the percentage of accuracy is 88. Novelty: One of the alternative diagnoses for potential COVID-19 disease is Recurrent Neural Network (RNN).
Mixed Reality Improves Education and Training in Assembly Processes Faieza Abdul Aziz; Adel S.M.A Alsaeed; Shamsuddin Sulaiman; Mohd Khairol Anuar Mohd Ariffin; Muhammad Faris Al-Hakim
Journal of Engineering and Technological Sciences Vol. 52 No. 4 (2020)
Publisher : Institute for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2020.52.4.10

Abstract

Mixed reality is the outcome of blending the physical world with the digital world, made possible by technological advancement. Mixed reality is the next evolution in human, computer, and environment interaction. Augmented reality (AR) uses a virtual model of the real world, augmented by using a computer to see the real environment through a special display device. Current education and training systems in the engineering maintenance field are still insufficiently directed at the psychomotor skills in learning about machine parts, which makes them less effective for trainees. The oil and gas industry always face problems related to inefficiency due to downtime of critical equipment. This study was conducted at designing and developing a virtual reality (VR) and augmented reality (AR) system as a learning and training platform. This work also reviewed AR applications for machine part maintenance and assembly. An AR system was modelled and developed using the following software: CATIA, Blender, Unity and Vuforia. The effectiveness of using the AR technique in an education and training process was evaluated with 20 respondents among university students. The results showed that using this AR app enhanced the participant's understanding according to certain criteria and can be adopted as a learning method.
Self-monitoring of Glucose With A Non-invasive Method Using Near Infrared Sensor Rinda Nur Hidayati; Nur Hasanah Ahniar; Gita Rindang Lestari; Atika Hendryani; Faris Al Hakim
SANITAS: Jurnal Teknologi dan Seni Kesehatan Vol 11 No 2 (2020): SANITAS Volume 11 Nomor 2 Tahun 2020
Publisher : Politeknik Kesehatan Kemenkes Jakarta II

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36525/sanitas.2020.10

Abstract

Diabetes mellitus or commonly called diabetes is a worldwide epidemic caused by chronic hyperglycemia. Simplify the blood glucose monitoring and easy to use is an essential part of diabetes management. Currently, the use of blood glucose meters conventional in clinical practice needs sufficient reliability. Therefore, self-monitoring of blood glucose with a non-invasive method was presented. A non-invasive blood glucose monitoring device was initially for information on glucose level measurements. A non-invasive method to determine the level of glucose by applying the physical properties of the absorption of the laser sensor that can produce a voltage change at various glucose levels. In this paper, a glucose monitoring module was fabricated with dimensions of 25x27x15 cm which has a minimum system, sensor, and LCD as a display of glucose levels. A minimum system to control the output of data digital value using microcontroller Android nano v.3. Experimentally, testing this module is by comparing the glucose monitoring modules that have been made with a gold standard. The result showed that non-invasive glucose monitoring is the potential for glucose level measurement a sensitivity, resolution, and accuracy of 0.86 mg/dL, 0.01 mg/dL, and 98.96%, respectively. The purposed module of glucose level monitoring offered simple testing for the rapid measurement of glucose levels.
The Role of Networks and Social Capital for Street Vendors on Jl. Laut Dendang, Deli Serdang Regency Widya Pangestika; Annisa M Nasution; Faris Al Hakim; Irwansyah P Marpaung; Nurhotma Tambak; Riski Ihsan
International Journal of Cultural and Social Science Vol. 2 No. 1 (2021): January
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (554.277 KB) | DOI: 10.53806/ijcss.v2i1.62

Abstract

Networks and social capital act as a binder for everyone in society. Social capital is an important issue because an economic business will be successful not only with financial capital, but also with the need for support from human resources, and social capital is one of its elements. Social capital refers to the main aspects of social organization such as trust, norms, networks, which are able to mobilize the participation of group members to achieve common goals. The street vendor who sells vegetables on the Dendang Sea Transportation Road is one of the street vendor communities in Deli Serdang Regency, which has survived in its business. The purpose of this study is to find out how trust is formed among street vendors, especially street vendors of vegetables on Jalan Laut Dendang and to find out the reciprocal relationship between street vendors on Jalan Laut Dendang. This type of research is descriptive qualitative, then the sample is based on purposive sampling technique (purpose sample) and 5 street vendors are determined. Analysis Techniques Data were analyzed qualitatively, based on theoretical support related to the object of research from respondents by means of observation and interviews. Then a conclusion is drawn regarding the results of the study. The main aspects of social capital that refer to trust, norms and networks that are seen in the vegetable street vendors (PKL) on the Dendang Sea Transportation Road show the value of social capital that is formed and interwoven between traders from the rules and regulations. Informal rules that apply in the merchant group they are able to obey together, even though there is no written agreement, so that these informal rules become separate norms that develop and are implemented together, reflecting the spirit of mutual giving, mutual trust, and the existence of networks. social network.
SOCIAL MEDIA COMMENTS FOR GOVERNMENT INSTITUTION VIDEO CLASSIFICATION USING MACHINE LEARNING M. Faris Al Hakim; Subhan Subhan; Prasetyo Listiaji
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5187

Abstract

YouTube is a social media site that is quite familiar and is used as a means of disseminating video-based information. With a fairly high number of users, YouTube can become a communication medium for audiences, including government agencies. The user’s responses in comments reflect the nuance of the presented video. This research aims to determine the best algorithm for classifying video types based on user comments. Several machine learning algorithms used to carry out classification are Decision Tree, Random Forest, K-Nearest Neighbor, Support Vector Machine, and Logistic Regression. K-Fold Cross Validation was chosen as a method to evaluate the performance of classification algorithms based on the accuracy values. of these algorithms in classifying YouTube videos based on comments. The first experiment with the highest ratio of training and test data for each algorithm was obtained at a ratio of 90:10, with respectively 78.99%, 86.21%, 84.01%, 72.72%, and 79.31%. In the second experiment with k-fold cross validation using a ratio of 90:10, the highest accuracy for each algorithm was obtained at a value of k = 10, which was respectively 74.39%, 81.34%, 78.05%, 85.21%, and 72.15%. From these results, it can be concluded that the most suitable algorithm for classifying YouTube videos based on comments is the Random Forest algorithm with a training and test data ratio of 90:10 and SVM with 10-cross-fold validation. These results show that a larger portion of data for learning has a positive impact on algorithm performance.
Room occupancy classification using multilayer perceptron Wijaya, Dandi Indra; Aulia, Muhammad Kahfi; Jumanto, Jumanto; Hakim, M. Faris Al
Journal of Soft Computing Exploration Vol. 2 No. 2 (2021): September 2021
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v2i2.53

Abstract

A room that should be comfortable for humans can create a sense of absence and appear diseases and other health problems. These rooms can be from boarding rooms, hotels, office rooms, even hospital rooms. Room occupancy prediction is expected to help humans in choosing the right room. Occupancy prediction has been evaluted with various statistical classification models such as Linier Discriminat Analysis LDA, Classification And Regresion Trees (CART), and Random Forest (RF). This study proposed learning approach to classification of room occupancy with multi layer perceptron (MLP). The result shows that a proper MLP tuning paramaters was able estimate the occupancy with 88.2% of accuracy
Rainfall prediction in Blora regency using mamdani's fuzzy inference system Damayanti, Dela Rista; Wicaksono, Suntoro; Hakim, M. Faris Al; Jumanto, Jumanto; Subhan, Subhan; Ifriza, Yahya Nur
Journal of Soft Computing Exploration Vol. 3 No. 1 (2022): March 2022
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v3i1.69

Abstract

In the case study of weather prediction, there are several tests that have been carried out by several figures using the fuzzy method, such as the Tsukamoto fuzzy, Adaptive Neuro Fuzzy Inference System (ANFIS), Time Series, and Sugeno. And each method has its own advantages and disadvantages. For example, the Tsukamoto fuzzy has a weakness, this method does not follow the rules strictly, the composition of the rules where the output is always crisp even though the input is fuzzy, ANFIS has the disadvantage of requiring a large amount of data. which is used as a reference for calculating data patterns and the number of intervals when calculating data patterns and Sugeno has the disadvantage of having less stable accuracy results even though some tests have been able to get fairly accurate results. In research on the implementation of the Mamdani fuzzy inference system method using the climatological dataset of Blora Regency to predict rainfall, it can be concluded as follows: (1) The fuzzy logic of the Mamdani method can be used to predict the level of rainfall in the city of Blora by taking into account the factors that affect the weather, including temperature, wind speed, humidity, duration of irradiation and rainfall. (2) Fuzzy logic for prediction with uncertain input values is able to produce crisp output because fuzzy logic has tolerance for inaccurate data. (3) The results of the accuracy of calculations using the Mamdani fuzzy inference system method to predict rainfall in Blora Regency are 66%.
Optimization of breast cancer classification using feature selection on neural network Jumanto, Jumanto; Mardiansyah, M Fadil; Pratama, Rizka Nur; Hakim, M. Faris Al; Rawat, Bibek
Journal of Soft Computing Exploration Vol. 3 No. 2 (2022): September 2022
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v3i2.78

Abstract

Cancer is currently one of the leading causes of death worldwide. One of the most common cancers, especially among women, is breast cancer. There is a major problem for cancer experts in accurately predicting the survival of cancer patients. The presence of machine learning to further study it has attracted a lot of attention in the hope of obtaining accurate results, but its modeling methods and predictive performance remain controversial. Some Methods of machine learning that are widely used to overcome this case of breast cancer prediction are Backpropagation. Backpropagation has an advantage over other Neural Networks, namely Backpropagation using supervised training. The weakness of Backpropagation is that it handles classification with high-dimensional datasets so that the accuracy is low. This study aims to build a classification system for detecting breasts using the Backpropagation method, by adding a method of forward selection for feature selection from the many features that exist in the breast cancer dataset, because not all features can be used in the classification process. The results of combining the Backpropagation method and the method of forward selection can increase the detection accuracy of breast cancer patients by 98.3%.
TheRêst: An Artificial Intelligence-Based Application for Digital Transformation of Qur’anic Understanding through Adaptive Tafsir and Thematic Sermons Utama, Reiki Aziz Yoga; Ilham, Ahmad Bagas Aditya; Hakim, M. Faris Al
Journal of Advances in Information Systems and Technology Vol. 7 No. 1 (2025): April
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v7i1.33906

Abstract

The advancement of digital technology and internet penetration has opened wide opportunities for the development of Islamic educational applications. However, most existing Qur’an applications remain static and function merely as digitized texts without adaptive features. This study introduces TheRêst, an AI-based mobile application designed to enhance Qur’anic understanding through adaptive tafsir summarization, thematic sermon generation, and contextual sermon analysis. The development followed the Agile methodology, allowing iterative design, development, and testing processes. Key features were built using Natural Language Processing (NLP) and evaluated through black-box testing and usability testing with the System Usability Scale (SUS). Functional testing confirmed that core features—such as tafsir summarization and sermon generation—performed as expected, although challenges remained in response time and long-text accuracy. Usability testing involved 20 participants, including preachers, students of Islamic studies, and general Muslim users. Results showed an average SUS score of 79.25, categorized as “Excellent,” indicating strong user acceptance and satisfaction. Qualitative feedback highlighted the clarity of the interface and the usefulness of AI-generated content, while also noting the need for faster processing. This research contributes to the field of digital Islamic education by demonstrating the feasibility of integrating AI into Qur’an applications, thus providing innovative solutions for preachers and learners. Future work will focus on refining AI models, improving performance, and adding collaborative features. Overall, TheRêst presents a significant step toward creating adaptive, user-centered digital tools that support a deeper and more contextual engagement with the Qur’an.
Co-Authors Adel S.M.A Alsaeed Ahniar, Nur Hasanah Annisa M Nasution Atika Hendryani Aulia, Muhammad Kahfi Budi Prasetiyo, Budi Damayanti, Dela Rista Faieza Abdul Aziz Gita Rindang Lestari Ilham, Ahmad Bagas Aditya Irwansyah P Marpaung Jumanto Jumanto, Jumanto Listiaji, Prasetyo Mardiansyah, M Fadil Mohd Khairol Anuar Mohd Ariffin Nurhotma Tambak Pradana, Fadli Dony Pratama, Rizka Nur Rawat, Bibek Reynaldi, Reynaldi Rinda Nur Hidayati Riski Ihsan S.Pd. M Kes I Ketut Sudiana . Shamsuddin Sulaiman Subhan Subhan Subhan Subhan Syafrizal, Wahyu Utama, Reiki Aziz Yoga Wicaksono, Suntoro Widya Pangestika Wijaya, Dandi Indra Yahya Nur Ifriza