Article Per Year (5 Year)
p-Index From 2021 - 2026
4.509
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TEKNIK INFORMATIKA Jurnal Sains dan Teknologi Jurnal Simetris Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Sisfokom (Sistem Informasi dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Jurnal Seni Tari Journal of Computer System and Informatics (JoSYC) Hanifiya: Jurnal Studi Agama-Agama Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Manajemen Informatika Jayakarta Bulletin of Computer Science Research J-SAKTI (Jurnal Sains Komputer dan Informatika) BEES: Bulletin of Electrical and Electronics Engineering JOMLAI: Journal of Machine Learning and Artificial Intelligence Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Journal of Artificial Intelligence and Digital Business Bulletin of Artificial Intelligence Ulil Albab Jurnal Manajemen, Pendidikan dan Ilmu Komputer (JMENDIKKOM)
Solikhun Solikhun, Solikhun
Unknown Affiliation
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences Other

Published : 47 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 47 Documents
Search

 1   2   3   4   5 
Rancang Bangun Sistem Presensi Biometrik Sidik Jari Berbasis IoT dengan Arduino Node MCU Kusuma, Rizky Tri; Solikhun, Solikhun; Saputra, Widodo; Windarto, Agus Perdana; Nasution, Della Fatricia
BEES: Bulletin of Electrical and Electronics Engineering Vol 4 No 3 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bees.v4i3.4636

Abstract

Presence is a procedure or process that needs to be carried out by all employees or members of a company or agency. The attendance register is a reference and benchmark for assessing, measuring, and determining the quality and quantity of each member or employee of the company or agency. Presentations carried out manually are considered less effective and efficient in terms of time and usage. So it is necessary to build an intelligent attendance system, one of which is by creating an intelligent attendance system that uses IoT-based fingerprint recognition technology with an Arduino controller and outputs to an Internet or intranet server. Based on the results obtained in the research, administrators can float their side servers freely. The time for sending data from the presence device to the server uses the POST method, with an average time of 8 seconds on an intranet network with a maximum distance of 25 meters from the wireless router or access point. By creating this attendance system, it makes it easier for the Surya Pematangsiantar Education Foundation to record the attendance of teachers and staff, especially SMK units.
Bone fracture classification using convolutional neural network architecture for high-accuracy image classification Solikhun, Solikhun; Windarto, Agus Perdana; Alkhairi, Putrama
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6466-6477

Abstract

This research introduces an innovative method for fracture classification using convolutional neural networks (CNN) for high-accuracy image classification. The study addresses the need to improve the subjectivity and limited accuracy of traditional methods. By harnessing the capability of CNNs to autonomously extract hierarchical features from medical images, this research surpasses the limitations of manual interpretation and existing automated systems. The goal is to create a robust CNN-based methodology for precise and reliable fracture classification, potentially revolutionizing current diagnostic practices. The dataset for this research is sourced from Kaggle's public medical image repository, ensuring a diverse range of fracture images. This study highlights CNNs' potential to significantly enhance diagnostic precision, leading to more effective treatments and improved patient care in orthopedics. The novelty lies in the unique application of CNN architecture for fracture classification, an area not extensively explored before. Testing results show a significant improvement in classification accuracy, with the proposed model achieving an accuracy rate of 0.9922 compared to ResNet50's 0.9844. The research suggests that adopting CNN-based systems in medical practice can enhance diagnostic accuracy, optimize treatment plans, and improve patient outcomes.
Analysis of Family Economic Factors on Students' Learning Interest Using the C4.5 Algorithm Rahayu, Dian; Solikhun, Solikhun; Sormin, Rizky Kairunnisa
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 2 No. 2 (2023): June
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v2i2.3195

Abstract

This study aims to analyze the influence of family economic factors on the learning interest of students at SMA Negeri 2 Pematangsiantar using the C4.5 algorithm. The C4.5 method is employed to identify the relationship between family economic variables and students' learning interest. The research is conducted at SMA Negeri 2 Pematangsiantar, involving students as the main respondents. Data is collected through a questionnaire covering family economic variables and the level of students' learning interest. Data analysis using the C4.5 algorithm assists in identifying family economic factors that significantly affect students' learning interest. The study's results are expected to provide a deeper understanding of the impact of family economic factors on student learning motivation in the high school environment. This research contributes to the education literature and offers insights for educators, parents, and education stakeholders to enhance support for students with diverse family economic backgrounds. The implications of these findings can aid in designing more inclusive education policies and supporting academic growth for high school students.
Penerapan Algoritma K-Means Dalam Mengelompokkan Jumlah Penerimaan Sinyal Telepon Seluler Di Sumatera Utara Sitompul, Wati Rizky Pebrianti; Solikhun, Solikhun; Saputra, Widodo; Oktaviani, Selli; windarto, agus perdana
Bulletin of Artificial Intelligence Vol 2 No 2 (2023): October 2023
Publisher : Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/buai.v2i2.137

Abstract

The purpose of this study was to cluster the number of cell phone signal reception in North Sumatra. The source of the data used is obtained from BPS. The variable used is the number of cell phone reception signals in North Sumatra. This research uses Data Mining Technique with K-means algorithm. It is hoped that the results of this study can provide input to the North Sumatra Province in order to determine the reception of cellular telephone signals, so as to increase the growth and development of telephone signal reception in North Sumatra. And 4G/LTE data obtained that there are 4 high clusters, namely (Mandailing Natal, Simalungun, Deli Serdang, Padang Lawas), 17 medium clusters, namely (Nias, South Tapanuli, Labuan Batu, Humbang Hasundutan, West Pakpak, Samosir, Labuhan Batu, South , Labuhan Batu Utara, North Nias, West Nias, Sibolga, Tanjung Balai, Pematangsiantar, Tebing Tinggi, Binjai, Padang Sidempuan, Gunung Sitoli), and there are 2 low clusters (Central Tapanuli, North Tapanuli, Toba, Asahan, Dairi, Karo , Langkat, South Nias, Serdang Bedagai, Batu Bara, North Padang Lawas, Medan).
Relevansi Konsepsi Rahmatan Lil Alamin dengan Keragaman Umat Beragama Solikhun, Solikhun
Hanifiya: Jurnal Studi Agama-Agama Vol 4, No 1 (2021): Hanifiya: Jurnal Studi Agama-Agama
Publisher : Program Studi Studi Agama-Agama Pascasarjana UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/hanifiya.v4i1.11487

Abstract

The impression of western world to the eastern has not shown a positive proportion. It always considers the latter as a minor. It is more so after the incident of WTC 9 September 2001. The eastern or as an assertive known as a terrorist, unhuman, and far from humane proportion. While in Islam itself, there is rahmatan li al-‘alamin. These things make the researcher interested in examining deeper what it means to be a rahmatan li al-‘alamin in Koran as a source of that word. Apart from the phrase rahmatan li al-‘alamin, a rahmat word is not connected to the li al-‘alamin word (universe) in the Koran. What also precisely is the difference between grace (rahmat) and rahmatan li al-‘alamin. Indonesia is a plural nation. That is not only ethnic, language, and custom tradition, but also religion. How is the relationship between faith in diversity frame? Is there relevance of li al-‘alamin in the diversity frame of religious people? Some questions need to search for answer in various reverence. The nation's founders have been proud not to obtrude by doing sharia for the adherents is a space for tolerance and religious people.
A revolutionary convolutional neural network architecture for more accurate lung cancer classification Muliadi, Muliadi; Windarto, Agus Perdana; Solikhun, Solikhun; Alkhairi, Putrama
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp516-526

Abstract

This research aimed to investigate a breakthrough in convolutional neural network (CNN) architecture with the potential to revolutionize lung cancer classification. The proposed method is a comparative optimization model of ResNet architecture, with accuracy rate of 99.68% in identifying and categorizing lung cancer types. The results showed that the use of innovative methods in CNN architecture, such as multi-dimensional convolutional layers and the integration of specific lung cancer features, effectively provided highly accurate and reliable outcomes. These showed a positive impact on the development of medical diagnostic technology, offering promising potential to enhance prognosis and response to treatment for lung cancer patients. With high accuracy rate, this breakthrough presents opportunities for further advancements in lung cancer management through artificial intelligence-based methods.
Jaringan Saraf Tiruan Untuk Memprediksi Tingkat Pemahaman Sisiwa Terhadap Matapelajaran Dengan Menggunakan Algoritma Backpropagation Solikhun, Solikhun; Safii, M.; Trisno, Agus
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 1, No 1 (2017): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (807.706 KB) | DOI: 10.30645/j-sakti.v1i1.26

Abstract

Prediction of students 'understanding of the subject is important to know the extent to which the students' understanding of the subjects presented by educators when teaching and learning activities and to determine the ability of educators in delivering subjects. Artificial Neural Network to predict the level of students' understanding of subjects using backpropagation learning algorithm uses several variables: Knowledge, skills / abilities, assessment and workload and guidance and counseling. Backpropagation learning algorithm is applied to train eight indicators to predict the level of students' understanding of the subjects. The test results obtained by the student's understanding level prediction accuracy rate of 90% with a 6-5-1 architecture.
Jaringan Saraf Tiruan Untuk Memprediksi Tingkat Pemahaman Sisiwa Terhadap Matapelajaran Dengan Menggunakan Algoritma Backpropagation Solikhun, Solikhun; Safii, M.; Trisno, Agus
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 1, No 1 (2017): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v1i1.26

Abstract

Prediction of students 'understanding of the subject is important to know the extent to which the students' understanding of the subjects presented by educators when teaching and learning activities and to determine the ability of educators in delivering subjects. Artificial Neural Network to predict the level of students' understanding of subjects using backpropagation learning algorithm uses several variables: Knowledge, skills / abilities, assessment and workload and guidance and counseling. Backpropagation learning algorithm is applied to train eight indicators to predict the level of students' understanding of the subjects. The test results obtained by the student's understanding level prediction accuracy rate of 90% with a 6-5-1 architecture.
Model Deep Learning Berbasis Inception V3 untuk Klasifikasi Penyakit Daun Apel Menggunakan Citra Digital Arifin Nur, Khairun Nisa; Wanto, Anjar; Windarto, Agus Perdana; Solikhun, Solikhun
Journal of Computer System and Informatics (JoSYC) Vol 6 No 3 (2025): May 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i3.7003

Abstract

Apple plants have high economic value, but their productivity is often disrupted by leaf diseases that can reduce quality and yield. Apple leaf disease identification is still largely performed manually, which is prone to errors and requires specialized expertise. Therefore, a method is needed to improve the accuracy and efficiency of apple leaf disease classification. This study aims to enhance the accuracy of apple leaf disease classification by implementing the Convolutional Neural Network (CNN) architecture, specifically Inception V3. The method involves collecting images of infected apple leaves, data preprocessing, and model training and evaluation. The results show that the Inception V3 model achieved an accuracy of 96%, which is higher than previous methods. The main advantage of this architecture lies in its ability to capture features at multiple scales simultaneously, improving the model’s ability to recognize disease patterns more accurately. With these findings, this study contributes to the development of AI-based plant disease detection technology and provides a practical solution for farmers to enhance apple farming productivity.
Comparison of Manhattan and Chebyshev Distance Metrics in Quantum-Based K-Medoids Clustering Solikhun, Solikhun; Siregar, Muhammad Rahmansyah; Pujiastuti, Lise; Wahyudi, Mochamad; Kurniawan, Deny
Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i4.5193

Abstract

Anemia is a condition characterized by a decrease in the number of red blood cells or hemoglobin levels in the bloodstream. It can lead to fatigue and reduced productivity. Clustering is a technique in data mining used to identify patterns that can support decision-making processes. In the case of anemia, clustering plays a crucial role in identifying various severity patterns and understanding the contributing factors behind the condition. Quantum computers, which utilize the principles of quantum mechanics for information processing, have made significant advancements over the past decade. Quantum computing is an advanced method of information processing that leverages qubits, enabling systems to exist in multiple states simultaneously. This technology offers the potential to solve complex problems at exponentially faster speeds than classical computers. In this study, researchers applied the K-Medoids clustering algorithm, calculated using quantum-based equations. The research compares two distance measurement methods: Chebyshev distance and Manhattan distance. The results show that the Manhattan algorithm performs better in medical contexts, particularly for detecting positive cases, with a recall of 0.57 and an F1-score of 0.695, although it has a slightly lower precision of 0.88. This makes it more suitable for medical applications where false negatives carry high risks, such as disease detection, despite its higher cost and mean squared error (MSE). On the other hand, Chebyshev distance achieved perfect precision (1.0) and higher accuracy (80%), but its low recall (0.33) indicates that many positive cases were missed. Therefore, Manhattan distance is more recommended for medical applications that require the detection of more positive cases, while Chebyshev is more efficient for scenarios that prioritize accuracy and cost.
Co-Authors Abdullah Ahmad Agus Perdana Windarto Aisyah, Nurfahana Siti Aisyah, Nurfahana Siti Akbari, Imam Alifah, Winanda Alkhairi, Putrama Amalya, Nanda Ambariyanto Ambariyanto Amri Amri Apriliani, Lulu Ardha, Mhd. Zoel Arifin Nur, Khairun Nisa Astuti, Wiwik Sri Batubara, Ela Roza Bintang Hanggoro Putra Buaton, Relita Carissa Arishandy, Dinda Dani, Siska Rama DENY KURNIAWAN Dian Rahayu Fatmawati, Kiki Fitri Rahayu Futihatus Sirriyah Gusfirullah, Icmi Hartama, Dedy Ht. Barat, Ade Ismiaty Ramadhona Hutasoit, Rahel Adelina Hutasoit, Rahel Adelina Imam Subekti Indri, Indriyani Iwan Purnama Kurniawan Kurniawan Kusuma, Rizky Tri Lubis, Ega Khairunnisa Lubis, Ega Khairunnisa Mochamad Wahyudi Muhammad Ilham Muhammad Raqib Syahkur Muhammad Ridwan Lubis Muliadi Napitupulu, Jessica Evonella Nasution, Della Fatricia Oktaviani, Selli Poningsih Poningsih Poningsih, Poningsih Pujiastuti, Lise Putri, Adelia Rafai, Muhammad Rahman Rahman Ramahdhani, Dwi Revi, Ahmad Revi, Ahmad Safii, M Safii, M Safii, M. Saputra, Widodo Sembiring, Rahmat Widia Simbolon, Astri Rismauli Siregar, Muhammad Rahmansyah Siregar, Sandy Putra Sitompul, Wati Rizky Pebrianti Sormin, Rizky Kairunnisa Sundari Retno Andani Trianda, Dimas Trisno, Agus Wanto, Anjar Weber, Gerhard-Wilhelm Widodo Saputra Wika, Wika Kasanova Yasin, Verdi