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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 Zero : Jurnal Sains, Matematika, dan Terapan JISTech (Journal of Islamic Science and Technology) JURNAL TEKNOLOGI DAN OPEN SOURCE JURNAL PENDIDIKAN TAMBUSAI 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 Health Information : Jurnal Penelitian Journal of Applied Engineering and Technological Science (JAETS) JSR : Jaringan Sistem Informasi Robotik Jatilima : Jurnal Multimedia Dan Teknologi Informasi JIKA (Jurnal Informatika) INFOKUM 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
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Nursing Physics Public Health Social Sciences Other

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Algoritma Genetika Untuk Perancangan Aplikasi Penjadwalan Mata Pelajaran Furqan, Mhd; Armansyah, A; Ananda, Rizki
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.476

Abstract

The schedule is one of the important activities to help the teaching and learning process in schools, the schedule planning process is still done manually so there are still conflicting schedules between classes. because of the large number of classes and a lot of time ordering a certain day so that sometimes up to 3 times the revision schedule, and the implementation of learning becomes late. To overcome this, one of the appropriate ones is used so that the scheduling process can run well. One of the algorithms used for scheduling the genetic algorithm is one of the improvement algorithms that can be used in various types of problems such as scheduling, the schedule will be tested on classes that clash, which are selected randomly. random or random in each class, the test will be asked to input or fill in the crossover probability number = 0.70 and mutation probability = 0.40 and the number of generations = 1000, then executed. After that it will occur and program execution in the form of selection, crossover, and mutation that will occur in the background of the screen, so that the results of applying 17 classes and 1 laboratory room using the genetic algorithm method can be used to compile a list of lessons.
Algoritma K-Means Untuk Segmentasi Kematangan Buah Jeruk Berdasarkan Kemiripan Warna Furqan, Mhd; Sriani, S; Aulia, Atiqah
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.437

Abstract

The condition of citrus fruits can be determined by looking at several parameters, one of which is color, larger pores, and even yellow skin. So far, the identification of the maturity level of citrus fruits by farmers and consumers has used manual techniques, for example paying attention to the color, pores and peel of the orange product. Such identification will be very large and fluctuating developmental days because people have visual impairments in recognizing, fatigue, and judgment on great development. Barriers to strategy guidance require innovations that can complete the development process impartially, and with clearer results. One of them is the segmentation process using yahoo k-means. The segmentation process aims to divide or separate the image into several (local) districts based on the specified attributes. The k-means algorithm will cluster data with similar attributes assembled into one set and data with various qualities assembled into different sets. From the results of taking pictures from 6 angles, namely front, back, top, bottom, and right and left using 8 datasets, it produces 48 images, and by testing the clustering results, ripe oranges produce 6 and 2 ripe.
Penentuan Kualitas Bibit Padi Menggunakan Metode Fuzzy Mamdani Furqan, Mhd.; Sriani, S; Hasugian, Abdul Halim; Hsb, Munawir Siddik
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.354

Abstract

the agriculture sector still faces fairly basic challenges, namely the quality problem and the increase in competitiveness through productivity and efficiency. This research determines the criteria for the best quality types of rice seeds and how to apply the Fuzzy Mamdani Method, to determine the quality of rice seeds in order to assist farmers in determining the quality of the best rice seeds. Mamdani fuzzy method is one example of a method that can help the optimal decision making process to solve practical problems. The problem solved is the determination of the best quality of rice seeds, based on established criteria, namely the type of rice, the shape of the rice, the color of the seeds, the age of the seeds, and roots. This is done to reinforce the output or output of each input variable membership. Then after the output input output variable is determined, the implementation of the rules for each parameter is carried out. After that do defuzzyfication with the centroid method. So that the output of one parameter is 60 with verry good information. This system was built with a website application where the application is able to help users to determine the quality of rice seeds and obtain information about the best seeds.
Classification of Scholarships for Students in Schools Using the Naïve Bayes Method Rizki Siregar, Awal; Furqan, Mhd.
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5417

Abstract

This research addresses the challenge faced by educational institutions in selecting scholarship recipients by implementing the Naïve Bayes algorithm. The objective of this study is to simplify and improve the accuracy of the scholarship selection process at MTs As-Syarif Kuala Beringin, using data from 50 students. The background highlights the importance of scholarships in providing equal educational opportunities, particularly for students with financial challenges. The research method involves the use of Naïve Bayes to calculate the probability of eligibility based on academic performance, economic background, and student activity. The results show that seven students met the scholarship criteria, demonstrating the efficiency and objectivity of the algorithm. The practical implications include the development of a user-friendly application that facilitates data input, scholarship criteria determination, and clear evaluation results. This system enhances transparency and reliability in decision-making. In conclusion, the Naïve Bayes algorithm proves to be an effective and efficient tool for scholarship selection, enabling a more equitable opportunity for students. Further research could focus on integrating additional data points or comparing the algorithm's performance with other classification methods to enhance system reliability.
Analisis Algoritma Sequential Search Pada Aplikasi Pencarian Berita Furqan, Mhd; Armansyah, A; Kurniawan, Riski Askia
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 8, No 2 (2023): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v8i2.622

Abstract

The algorithm is an approach to be able to compile and manage data efficiently. The Sequential Search algorithm is used to build a mobile-based news search application. The search function is used to validate the data itself. The Sequential Search algorithm has 2 possibilities, namely the best possibility (best case) and the worst case (worst case). In determining a possibility, it takes the complexity of the time algorithm. This study will analyze the Sequential Search algorithm in determining the 2 possibilities that occur in mobile applications. Data is one of the things needed in the development of an application. There are 100 data used by researchers as keywords and researchers will take 5 keywords, namely Earthquake, PDI, Indonesian Education, Floods, and Online Sales to determine the best case and worst case from the Sequential Search algorithm. In determining the speed of time required running time program in units of milliseconds (ms). So that the average time for the earthquake keyword is 0.014189 ms, the PDI keyword is 0.073763 ms, the Indonesian Education keyword is 0.169640 ms, the Flood keyword is 0.206307 ms, and the Online Selling keyword is 0.284086 ms. By obtaining the time from the test, the results of the complexity are also obtained, namely Tmin(n) = 0.014189 ms so that the best case is found in the Earthquake keyword and Tmax(n) = 0.284086 ms so that the worst case is found in the Online Selling keyword. And Tavg(n) = 0.1491375 ms. News API is HTTP REST API which is used to access news after keywords are found
Classification Of Rice Plant Diseases Using K-Nearest Neighbor Algorithm Based On Hue Saturation Value Color Extraction And Gray Level Co-Occurrence Matrix Features Siti Saniah; Mhd. Furqan
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i2.3972

Abstract

This research aims to classify diseases in rice plants using the K-Nearest Neighbor (K-NN) algorithm based on Hue Saturation Value (HSV) color feature extraction and Gray Level Co-Occurrence Matrix (GLCM) texture. The main problem faced is how to identify the type of disease in rice plants automatically using digital images. Diseases such as Blight, Tungro, and Crackle often attack rice plants and require an accurate early detection system. Lack of understanding in recognizing disease symptoms manually often leads to errors in handling. For this reason, this research develops an image processing-based classification system that can detect diseases such as Blight, Tungro, and Crackle. The method used in this research is image processing which includes RGB to HSV color space conversion, texture feature extraction using GLCM, and classification using K-NN algorithm. The dataset consists of 240 images, divided into training data and testing data, namely 192 training data and 48 testing data. Tests were conducted by calculating accuracy at various values of the K parameter, namely K = 1, K = 3, and K = 5, to determine the effectiveness of the model in classifying plant diseases. The purpose of this study was to evaluate the accuracy of the system in identifying rice diseases and test the combination of HSV and GLCM features in improving classification performance. The results showed that using HSV and GLCM features together resulted in the highest accuracy at K=3 with an accuracy value of 75%. The system is expected to assist farmers in detecting plant diseases quickly and effectively, thus minimizing production losses and supporting agricultural sustainability
Analisis Sentimen Pengguna X terhadap Kebijakan PPN 12% Menggunakan Naive Bayes Panggabean, Alwi Andika; Kartikasari, Diah Putri; Aulia, Rafif Risdi; Tambak, Tiara Ayu Triarta; Nabila, Siti Fadiyah; Furqan, Mhd
Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer) Vol 5 No 1 (2025): Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitekt
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakadata.v5i1.1002

Abstract

Kebijakan kenaikan Pajak Pertambahan Nilai (PPN) dari 11% menjadi 12% yang direncanakan berlaku pada tahun 2025 telah menimbulkan berbagai reaksi publik, terutama di media sosial. Penelitian ini bertujuan untuk menganalisis sentimen pengguna media sosial X (sebelumnya Twitter) terhadap kebijakan tersebut menggunakan metode Naive Bayes yang diimplementasikan dalam bahasa pemrograman R. Data diperoleh dari tweet yang relevan dengan topik PPN 12%, kemudian diproses melalui tahapan pra-pemrosesan dan pelabelan manual. Hasil analisis menunjukkan bahwa sentimen negatif mendominasi dengan proporsi 39%, diikuti sentimen netral 32%, dan sentimen positif 29%. Evaluasi performa model Naive Bayes menunjukkan akurasi sebesar 50%, dengan ketepatan klasifikasi tertinggi pada kategori negatif. Analisis lebih lanjut terhadap istilah kunci dan topik diskusi mengungkapkan bahwa kekhawatiran terhadap beban ekonomi dan dampak terhadap UMKM menjadi sumber utama sentimen negatif, sementara sentimen positif dikaitkan dengan harapan terhadap perbaikan layanan publik dan pembangunan. Penelitian ini memberikan wawasan penting bagi pembuat kebijakan untuk memahami persepsi publik terhadap kebijakan fiskal secara lebih mendalam dan berbasis data.
Analisis Data Biologis dalam Mengidentifikasi Gen atau Protein yang Memiliki Pola Ekspresi Serupa Akmal, Muhammad Haikal; Pangestu, Dimas; Siregar, Dzilhulaifa; Harahap, Khaila Mukti; Furqan, Mhd.
Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer) Vol 5 No 1 (2025): Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitekt
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakadata.v5i1.1008

Abstract

Ekspresi protein dalam data biologis umumnya memiliki kompleksitas tinggi dan dimensi besar, sehingga menyulitkan pengenalan pola secara langsung. Studi ini memanfaatkan algoritma Spectral Clustering untuk mengeksplorasi struktur tersembunyi dalam kumpulan data ekspresi protein. Langkah awal mencakup pembersihan data dengan imputasi nilai hilang menggunakan metode rata-rata serta normalisasi fitur numerik menggunakan StandardScaler. Dataset terdiri dari 1.080 observasi dan 77 atribut numerik hasil percobaan pada tikus. Proses pengelompokan dilakukan dengan pendekatan berbasis graf, menggunakan parameter empat klaster dan afinitas nearest neighbors. Selanjutnya, dilakukan reduksi dimensi melalui teknik Principal Component Analysis (PCA) untuk menghasilkan representasi dua dimensi yang mudah divisualisasikan. Hasil pengelompokan memperlihatkan pemisahan yang mencerminkan perbedaan biologis antar sampel. Hal ini menunjukkan bahwa metode tak terawasi seperti Spectral Clustering efektif dalam mengungkap struktur laten pada data ekspresi protein dan dapat menjadi dasar bagi analisis klasifikasi berbasis karakteristik biologis.
Analisis Sentimen Terhadap Tindakan Pemerintah Indonesia Untuk Menampung Sementara Pengungsi Etnis Rohingya Menggunakan Naive 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 Naive 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%.
Sentiment analysis of Faculty of Science and Technology students' satisfaction with the 2024 graduation using the Naïve Bayes method Siregar, Kalfida Eka Wati; Ramadani, Wily Supi; Sitepu, Anggi Jelita; Fadil, Ulfi Muzayyanah; Furqan, Mhd.
Internet of Things and Artificial Intelligence Journal Vol. 5 No. 2 (2025): Volume 5 Issue 2, 2025 [May]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v5i2.940

Abstract

Sentiment analysis of UINSU student graduation based on academic data is one of the efforts to understand the factors that affect the success of student studies. This research aims to analyze the sentiment of UINSU student graduation by utilizing academic data such as cumulative grade point average (GPA), number of credits taken, and other relevant attributes, using the Naive Bayes method. Naive Bayes was chosen because of its ability to classify data efficiently and accurately, even though the data used has noise or inconsistency. The research process begins with collecting student data from the university database, and then data cleaning is carried out to ensure the quality of the data used. Next, the data is processed and classified using the Naive Bayes algorithm in Weka software to predict graduation status based on academic parameters. The results show that the Naive Bayes method is able to produce quite high accuracy in predicting student graduation, with accuracy values ranging from 75% to more than 85% depending on parameter selection and data cleaning. GPA is the most influential attribute on the prediction results, while other attributes such as class activity and organizational experience also contribute, although not as much as GPA. These findings provide important insights for the campus in designing more effective academic coaching and planning programs and can be a reference in the development of data mining-based decision support systems to improve the quality of computer science graduates.
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 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 Fauzan Irsyad FIKRI HAIKAL Gunawan, Irwan Harahap, Khaila Mukti Harahap, Raihan Rizieq Harahap, Rosa Linda Hasrul Hasibuan, Mhd Fikri 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 Maulana Ihsan, Maulana Mey Hendra Putra Sirait Mhd Ikhsan Rifki Mhd Reza Alfani Muhammad Akbar Ramadhan Tanjung Muhammad Farhan Muhammad Ikhsan Muhammad Naufal Shidqi Muhammad Ridzki Hasibuan Muhammad Rizki Munadi Munadi Nabawy, Putri Nabila, Siti Fadiyah Nasution, Afri Yunda Nasution, Irma Yunita Nasution, Zulia Lestari Ningsih, Siti Alus Nugroho, Agung Nurhasanah Nurhasanah Nurhidayati Nurhidayati Nurul Hadi Muliani Hariadi Saputra Nurzannah, Laila 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 Ramadhani, Fredy Kusuma Razzaq H. Nur Wijaya Reza Muhammad Rifnandy, Muhammad Fauzan 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 Wulandari Suhardi Suhardi Suhardi Suhardi Suhardi, S Susan Mayang Sari Syamia, Nanda Tambak, Tiara Ayu Triarta Tanjung, Tegar Haryahya Toibatur Rahma Matondang Tria Elisa Wan Fadilla Rischa Wati, Putri Kurni Widiya Yuli Kartika Siregar Yusuf Ramadhan Nasution Yusuf Ramadhan Nasution, Yusuf Ramadhan Zabni, Nur Hera Ziqra Addilah