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All Journal JURNAL SISTEM INFORMASI BISNIS Techno.Com: Jurnal Teknologi Informasi Scientific Journal of Informatics CESS (Journal of Computer Engineering, System and Science) Sinkron : Jurnal dan Penelitian Teknik Informatika JISTech (Journal of Islamic Science and Technology) JURNAL TEKNOLOGI DAN OPEN SOURCE JURNAL PENDIDIKAN TAMBUSAI Jurnal Nasional Komputasi dan Teknologi Informasi J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Mantik JISKa (Jurnal Informatika Sunan Kalijaga) Technologia: Jurnal Ilmiah Jurnal Ilmu Komputer dan Bisnis Health Information : Jurnal Penelitian Journal of Applied Engineering and Technological Science (JAETS) JSR : Jaringan Sistem Informasi Robotik Jatilima : Jurnal Multimedia Dan Teknologi Informasi Journal of Computer System and Informatics (JoSYC) JIKA (Jurnal Informatika) INFOKUM Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) El-Qist : Journal of Islamic Economics and Business (JIEB) Journal of Computer Networks, Architecture and High Performance Computing Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) IJISTECH Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Walisongo Journal of Information Technology Syntax: Journal of Software Engineering, Computer Science and Information Technology Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Instal : Jurnal Komputer Jurnal Teknisi J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Mandiri IT Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer JOMLAI: Journal of Machine Learning and Artificial Intelligence Data Sciences Indonesia (DSI) Internet of Things and Artificial Intelligence Journal Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Mhd Furqan
Universitas Islam Negeri Sumatera Utara
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Other

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Tanjung, Tegar Haryahya Classification of Heart Disease Using Support Vector Machine Tanjung, Tegar Haryahya; Furqan, Mhd
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13904

Abstract

Heart disease is a disease that has a high mortality rate, with more than 12 million deaths occurring throughout the world. Diagnosis of heart disease is very challenging due to the complex interdependence of several attribute factors. The problem that frequently encountered is the lack of accuracy in the classification process. Thus, a system is needed to carry out early diagnosis of heart disease. The structure of this research is to take a heart disease dataset from Kaggle. Then the data will be cleaned with preprocessing. The preprocessing process carried out is changing table names, checking missing values, and normalizing. 820 data will be trained using a Support Vector Machine and 205 data will be tested to find out how well the model can perform classification. The results of training and testing from a total of 1025 data will form a classification model. The model formed using the Support Vector Machine obtained confusion matrix results of 88 is True Positive data, 93 is True Negative data, 10 is False Positive data, and 14 is False Negative data. So the results of model training produce an accuracy of 88%.
Face-Based Attendance Data Using Principal Component Analysis Aulia, Muhammad Arief; Furqan, Mhd.; Sriani, S
IJISTECH (International Journal of Information System and Technology) Vol 7, No 5 (2024): The February edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i5.331

Abstract

The face is one of the easiest physiological measurements and is often used to distinguish the identity of one individual from another. This facial recognition process uses raw information from image pixels produced through the camera which is then represented in the Principal Components Analysis method. The way the Principal Components Analysis method works is by calculating the average flatvector pixel of images that have been stored in a database, from the average flatvector the eigenface value of each image will be obtained and then the closest eigenface value of the image will be searched for. image of the face you want to recognize. The test results show the overall success rate of face recognition that the application can carry out face recognition using digital camera hardware for the attendance system by displaying the name of the face owner as well as the date and time of recognition. The average accuracy value of the test with the light intensity level is 96.66%, the accuracy value The average test value with changes in the distance between the camera and the face was 98.33% and the average accuracy value of the test using glasses and hat accessories was 85%.
Sentiment Analysis Of Instagram Social Media Users For BPJS Health Services Using Support Vector Machine Algorithm Hsb, Dinda Umami; Furqan, Mhd; Armansyah, A
IJISTECH (International Journal of Information System and Technology) Vol 8, No 1 (2024): The June edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i1.349

Abstract

Health services are an important aspect of people's quality of life, and BPJS as a public health service provider in Indonesia is often the subject of discussion on social media platforms. The SVM method has proven effective in sentiment analysis in various domains, including social media. In this study, data in the form of user comments and uploads on BPJS Instagram accounts were collected and processed to identify Positive, Negative or Neutral sentiments regarding the health services provided by BPJS. with the government's efforts to improve access and quality of health services for pregnant women as well as provide financial protection in order to reduce maternal and infant mortality in Indonesia and has the aim of reducing the burden of childbirth costs for people with low and middle incomes. This information can be input for BPJS in improving quality according to public expectations. In this research with a data set of 600 comments, the research was carried out with the support vector machine classification and the highest accuracy results in the first test experiment on 80% training data and 20% test data with 97% precision, 64% recall and 77% F1-Score obtained accuracy by 83%.
Determination of The Closest Path Using The Greedy Algorithm Furqan, Mhd.; Adha, Rifki Mahsyaf; Armansyah, A
IJISTECH (International Journal of Information System and Technology) Vol 7, No 5 (2024): The February edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i5.332

Abstract

Several alternate routes are displayed by the greedy algorithm, which is widely used in the closest travel route search application. This study employs the greedy method, which sets up a route map to quickly determine the shortest path. The goal of this study is to find the shortest path using a greedy algorithm. By using a greedy algorithm system to find the closest point to which the user's selection is made, the study's eight times with different points on the graph can be seen in the user's position. In an attempt to find the best solution, the greedy algorithm—which is renowned for its simplicity and effectiveness—iteratively chooses the best option available at each step. The greedy algorithm frequently gives priority to proximity when it comes to travel route optimization, and it might not always produce the shortest path overall. However, it's a well-liked option for some applications due to its quickness and simplicity of implementation. Notwithstanding its drawbacks, the greedy algorithm can offer insightful solutions for optimization and route planning issues. Users can make decisions more quickly and possibly find alternate routes they might not have otherwise thought of by using this algorithm to find the closest point in a travel route search application. The study's conclusions also emphasize how crucial it is to take user convenience and preferences into account when developing route planning systems. Future studies could look into ways to improve the greedy algorithm's performance and fix its drawbacks, like adding more heuristics or combining it with other optimization strategies. Overall, this study's findings validate the greedy algorithm's efficacy as a workable choice for locating the closest point in travel route search applications, providing consumers with a dependable and approachable navigational aid.
Application of The K-Means Clustering Algorithm to Identify Strawberry Fruit Ripe Rizki, Muhammad; Furqan, Mhd; Sriani, S
IJISTECH (International Journal of Information System and Technology) Vol 8, No 2 (2024): The August edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i2.356

Abstract

Fruit ripeness will usually be determined by several parameters, including size, weight, color characteristics, fragrance, etc. The parameter of fruit ripeness in terms of fruit skin color is one of the important factors in identifying fruit maturity. Segmentation is a method in digital image processing to differentiate objects in an input image. The general classification process is carried out by looking directly at the fruit object. The purpose of this research is to create an analysis in identifying the ripeness of strawberry fruit and designing an application system that can identify the ripeness of strawberry fruit. This application was built with the MATLAB application. The methods used include K-Means Clustering segmentation, labeling and feature extraction. The detection of the type of fruit is carried out using feature matching at the level of shape and color. Before classifying the name of the type of fruit and the level of maturity, the fruit training must be carried out first and then continued with fruit detection and identification of maturity. Based on the results of the strawberry image maturity identification test with six test strawberry images consisting of three types of maturity levels, the results were obtained, namely mature test one and mature test two levels of ripeness and correct identification results, half ripe test one and half ripe test two levels of ripeness and results Correct understanding, raw test one and raw test two mature levels and correct recognition. Meanwhile, the accuracy test results obtained an accuracy value of 100% for identifying the maturity of strawberry images. From the results of the tests carried out, it can be concluded that identification of ripeness in strawberry fruit images was successfully applied using the K-Means Clustering method on images of ripe, half-ripe and unripe strawberries. And from testing the identification of ripeness of strawberry fruit with test data of six images and training data of twelve images, it gave an accuracy result of 100%.
Decision Support System For Chicken Animal Feed Selection Using The Fuzzy Tsukamoto Method Wahyuni, Sri; Furqan, Mhd.; Lubis, Aidil Halim
IJISTECH (International Journal of Information System and Technology) Vol 7, No 5 (2024): The February edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i5.330

Abstract

Selection of chicken feed is a fairly important decision support process. This decision support system was developed to assist breeders in choosing chicken feed based on predetermined criteria and alternatives. In this study using the Tsukamoto fuzzy method which produces a model of a system that can provide recommendations for choosing chicken feed that is applied in a decision support system. The Tsukamoto fuzzy method in determining the selection of chicken feed is based on 3 variables, namely price, quality and stock. Each variable consists of 3 sets which are combined in order to obtain 4 fuzzy rules, which are then used in the inference stage. The choice of chicken feed to be recommended (z) is searched by the centralized average defuzzification method. Testing will be carried out objectively where the decision support system is tested directly for capacity and filling out a questionnaire regarding satisfaction with the content of the point requirements and distributed to the owner of the animal feed shop. With this test, it can be seen that the features provided are easy to learn and easy to understand.
Decision Support System for Selection of Scholarship Recipient Students Using Tsukamoto Method Fuzzy Logic Anwar, Mufti Husain; Furqan, Mhd.; Suhardi, S
IJISTECH (International Journal of Information System and Technology) Vol 8, No 1 (2024): The June edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i1.341

Abstract

Scholarship selection is an important process in determining the acceptance of educational assistance based on established criteria. Decision Support Systems (DSS) have proven their value in assisting complex decision making. In the context of scholarship selection, Tsukamoto's fuzzy approach in decision support systems is an effective tool for modeling the complexity and uncertainty of selection criteria. This system utilizes fuzzy logic to connect variable inputs such as academic achievement, financial condition, and other factors to output in the form of membership values in the scholarship acceptance category. The fuzzy rules defined by educational experts and practitioners enable the system to interpret conditions that are closer to human thinking. Thus, Tsukamoto's fuzzy approach in decision support systems becomes an effective tool in simplifying the complexity of scholarship selection, while providing data-based solutions. More objective and comprehensive.
Application of Data Mining to Predict Birth Rates in Medan City Using the K-Nearest Neighbor Method Putri, Alma Irawanti; Furqan, Mhd.; Suhardi, Suhardi
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v5i1.17991

Abstract

The birth rate of babies in Indonesia tends to increase every month, based on this fact, the population in Indonesia is increasing over time. One of the contributing factors is increasingly sophisticated technology, so that a country's birth rate can be accelerated, and if this event occurs continuously it will have an impact on population density which will occur notlonly inlIndonesia, butlalso throughoutylthelworld. Therefore, birth rate predictions are needed for planning and public policy in the fields of health and social welfare. One of them is using data mining techniques to predict the number of births in Medan City using the KNN method. KNN is a classification method based on the neighborhood value between training data and test data. Thelpredictioniresults will beicompared withlactual datalto measure thekaccuracy of predictions on birth data totaling 131 data. The accuracy results obtained were 83.9% with a total of 4,413 births and 8,485 pregnant women 
Analisis Sentimen Terhadap Tindakan Pemerintah Indonesia Untuk Menampung Sementara Pengungsi Etnis Rohingya Menggunakan Naïve Bayes Classifier Gunawan, Irwan; Furqan, Mhd.
CESS (Journal of Computer Engineering, System and Science) Vol 9, No 2 (2024): July 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v9i2.61808

Abstract

Etnis Rohingya merupakan penduduk asli di negara myanmar yang sebagian besar mayoritasnya beragama muslim. Konflik yang terjadi pada etnis tersebut dimulai sejak ditetapkannya kebijakan Burma Citizen Law oleh pemerintah myanmar. kebijakan ini berisi terkait penolakan pemerintah myanmar terhadap etnis Rohingya sebagai etnis resmi dan memutuskan jika etnis tersebut tidak termasuk dari negara Myanmar. Indonesia merupakan salah satu negara di ASEAN yang masih menampung sementara pengungsi Rohingya, tindakan ini dilakukan berdasarkan konsep Human Security dan mengacu pada Peraturan Presiden Republik Indonesia Nomor 125 Tahun 2016 Tentang Penanganan Pengungsi Dari Luar Negeri Pasal 4 Ayat 2 mengenai koordinasi penanganan pengungsi yang meliputi Penemuan, Penampungan, Pengamanan dan Pengawasan. Akibatnya, terjadinya cemburu sosial yang berdampak pada keberagamannya opini masyarakat dan menjadi isu yang sering dibicarakan. Penelitian ini bertujuan untuk mengetahui kecenderungan opini berdasarkan klasifikasi sentimen yang diperoleh melalui video YouTube. Manfaat dari penelitian ini adalah agar pemerintah indonesia dapat mengetahui tindakan tersebut cenderung positif atau negatif. Dalam penelitian ini menerapkan algoritma Naïve Bayes Classifier dengan dataset berjumlah 7547 yang dibagi menjadi 6037 data latih dan 1510 data uji. Hasil Confussion Matrix pada penelitian ini menunjukan akurasi 93%.
Penerapan Data Mining dalam Pengelompokan Kualitas Produk Kelapa Sawit Menggunakan Algoritma K-Means Clustering Putra, Suan Ekie Nanda; Furqan, Mhd.
CESS (Journal of Computer Engineering, System and Science) Vol 9, No 2 (2024): July 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v9i2.61682

Abstract

Minyak kelapa sawit banyak digunakan dalam berbagai produk, termasuk makanan, kosmetik, dan biodiesel. Untuk menjaga kualitas produk, diperlukan pemantauan serta analisis data secara terperinci sangat penting. Pada PT. Sri Ulina Ersada Karina, proses produksi Crude Palm Oil saat ini hanya mengikuti standar nasional tanpa analisis lebih lanjut tentang kualitas produk. Dengan analisis yang lebih mendalam, perusahaan dapat meningkatkan efisiensi dan mutu produk. Penelitian ini bertujuan untuk menerapkan teknik data mining, khususnya algoritma K-Means Clustering, untuk mengelompokkan kualitas produk kelapa sawit yang diolah menggunakan tools Jupyter Notebook. Hasil dari penelitian ini menghasilkan 3 cluster yaitu cluster 0 kategori baik dengan jumlah data sebanyak 89 sampel, Cluster 1 kategori kurang baik dengan jumlah data sebanyak 72 sampel, dan Cluster 2 kategori sangat baik dengan jumlah data sebanyak 132 sampel.
Co-Authors Abdul Aziz Abdul Halim Hasugian Adha, Rifki Mahsyaf Agpina, Pipi Ahmad Fakhri Ab. Nasir Ahmad Fauzi Aidil Halim Lubis Aisyah Nurrahmah Siregar Akmal, Muhammad Haikal Anwar, Mufti Husain Apriansyah, Yuda Ardyanti, Tiwy Armansyah Armansyah Armansyah Armansyah Armansyah Armansyah Armansyah, A Aulia, Atiqah Aulia, Muhammad Arief Aulia, Muhammad Fathir Aulia, Rafif Risdi Badria, Lailatul Bagus Ageng Alfahri Br Rambe, Indri Gusmita Cahyadi, Bhagaskara Daulay, Ikhsan Agus Martua Elce, Furkan Fadil, Ulfi Muzayyanah Fadillah, Rini Fahrul Azis Nasution Faiza, Nayla Fakhriza, M. fandi, Fandi Ahmad FIKRI HAIKAL Gunawan, Irwan Harahap, Khaila Mukti Harahap, Raihan Rizieq Harahap, Rosa Linda Hasrul Hasibuan, Mhd Fikri Heri Santoso Himawan Hasibuan, Riswanda Ichsan HP, Kiki Iranda Hsb, Dinda Umami Hsb, Munawir Siddik Hutagalung, Muhammad Wandisyah R Ilham Fuadi Nasution Imam Zaki Husein Nst Iskandar, Rozai Ismail Pulungan Januar, Bagus juwita sari K Khairunnisa Kartikasari, Diah Putri Khairi, Nouval Khairunnisa Khairunnisa Khairunnisa, K Kurniawan, Riski Askia Lely Sahrani Lubis, Akbar Maulana M. Fakhriza Mahendra, Rifandi Matondang, Toibatur Rahma Maulana Ihsan, Maulana Mey Hendra Putra Sirait Mhd Ikhsan Rifki Mhd Reza Alfani Muhammad Akbar Ramadhan Tanjung Muhammad Farhan Muhammad Ikhsan Muhammad Luthfi Muhammad Naufal Shidqi Muhammad Ridzki Hasibuan Muhammad Rizki Munadi Munadi Nabawy, Putri Nabila, Siti Fadiyah Nasution, Afri Yunda Nasution, Irma Yunita Nasution, Romaito Nasution, Zulia Lestari Ningsih, Siti Alus Novrianty, Amanda Nugroho, Agung Nur Bainatun Nisa Nurhasanah Nurhasanah Nurhidayati Nurhidayati Nurul Hadi Muliani Hariadi Saputra Nurzannah, Laila Pane, Putri Pratiwi Pangestu, Dimas Panggabean, Alwi Andika Pratama, Haris Prayoga Elfanda Fachmi Hasibuan Prayogi, Ahmad Pulungan, Miftahul Rizky Putra, Suan Ekie Nanda Putri, Alma Irawanti Raissa Amanda Putri Rakhmat Kurniawan R Ramadani, Wily Supi Ramadhan Nasution, Yusuf Ramadhani, Fredy Kusuma Razzaq H. Nur Wijaya Reza Muhammad Rifnandy, Muhammad Fauzan Rivaldi Prima Nanda Rizka Rizki Ananda Rizki Siregar, Awal Rizqi Hidayat Tanjung RR. Ella Evrita Hestiandari Saparuddin Siregar Saputri Nasution, Intan Widya Sembiring, Yogasurya Pranantha Shafa, Dafa Ikhwanu Sinaga, Meri Siregar, Dzilhulaifa Siregar, Hervilla Amanda R. Siregar, Kalfida Eka Wati Sitepu, Anggi Jelita Siti Saniah Siti Sarah Harahap Siti Sumita Harahap Sitorus, Nur Shafwa Aulia Solly Aryza Sri Rahmadani Sri Wahyuni Sriani Sriani Sriani Sriani Sriani, S Suci Syahputri Suci Wulandari Suhardi, S Suhardi, Suhardi Susan Mayang Sari Syamia, Nanda Tambak, Tiara Ayu Triarta Tanjung, Tegar Haryahya Tria Elisa Wan Fadilla Rischa Wati, Putri Kurni Widiya Yuli Kartika Siregar Yusuf Ramadhan Nasution Yusuf Ramadhan Nasution, Yusuf Ramadhan Zabni, Nur Hera Zahra Humaira Kudadiri Ziqra Addilah