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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Informatika dan Teknik Elektro Terapan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Warta Pengabdian Antivirus : Jurnal Ilmiah Teknik Informatika Jurnal ICT : Information Communication & Technology IJAIT (International Journal of Applied Information Technology) JATI (Jurnal Mahasiswa Teknik Informatika) JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Jurnal Abdi Insani Jurnal Sisfotek Global Innovation in Research of Informatics (INNOVATICS) Informatics and Digital Expert (INDEX) PROFICIO: Jurnal Pengabdian Masyarakat Journal of Community Practice and Social Welfare (JaCiPS) JURNAL ELEKTRO DAN INFORMATIKA SWADHARMA (JEIS) Jurnal Sisfotek Global Kohesi: Jurnal Sains dan Teknologi Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Jurnal Ilmiah Informatika dan Komputer
Sudiarjo, Aso
Universitas Perjuangan Tasikmalaya
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Journal : innovation in research of informatics innovatics

 1 
Perbandingan Algoritma Naïve Bayes Classifier dan Algoritma Decision Tree untuk Analisa Sistem Klasifikasi Judul Skripsi Rasi Nuraeni; Aso Sudiarjo; Randi Rizal
Innovation in Research of Informatics (INNOVATICS) Vol 3, No 1 (2021): Maret 2021
Publisher : Informatika Universitas Siliwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v3i1.2976

Abstract

Penelitian ini mengkaji tentang perbandingan klasifikasi judul skripsi menggunakan algoritma naïve bayes classifier dan algoritma decision tree. Tujuan dari penelitian ini adalah untuk membandingkan dua algoritma dalam pengklasifikasian judul skripsi. Proses pengumpulan data dilakukan dengan cara studi pustaka dan literature sejenis. Hasil pengumpulan data akan di analisis dengan menggunakan algoritma naïve bayes classifier dan algoritma decision tree dengan tools rapidminer. Hasil penenlitian ini menemukan perbandingan yang cukup signifikan dengan hasil akurasi 80,33% untuk algoritma naïve bayes classifier dan 60,33% untuk algoritma decision tree dari 52 data judul skripsi yang digunakan.
Implementation of Data Mining at Laboratory Vocational High School Using The C4.5 Algorithm to Predict Students Major Preferences Suherman, Nurisya Rahma; Ruuhwan, Ruuhwan; Sudiarjo, Aso
INNOVATICS: International Journal on Innovation in Research of Informatics Vol 5, No 2 (2023): September 2023
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v5i2.8479

Abstract

Education or the learning process is the primary thing for human life. Therefore, a place for acquiring knowledge is established, which is called a school. Schools have their own levels, ranging from early childhood education to higher education institutions. When students enter high school, they are required to make decisions in choosing their majors. Accompanied by technological advancements, the issues in high school major selection can be effectively and efficiently addressed using data mining. Common issues that usually arise include lack of accuracy, precision, and requiring a significant amount of time. Hence, the issues within major selection necessitate the use of data mining, employing the C4.5 algorithm method, to determine the accuracy and precision of large datasets. This research achieved with RapidMiner the result is accuracy score of 94.44%, precision of 81.37%, and sensitivity of 74.00%. Additionally, it also generated a decision tree and with Python has an accuracy of 93% because it automatically rounds the values, so there is no significant difference between the two tools. This proves that the C4.5 algorithm produces fairly accurate performance.
Analysis of Image Improvement and Edge Identification Methods in Watermelon Image Sudiarjo, Aso; Praseptiawan, Mugi; Setyoningrum, Nuk Ghurroh; Drajat, Hilmi Maulana; Natsir, Fauzan
Innovation in Research of Informatics (Innovatics) Vol 6, No 1 (2024): March 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i1.10699

Abstract

The initial stage in digital image processing, known as pre-processing, plays a vital role in enhancing image quality. This essential step involves employing various techniques to prepare the image for subsequent analysis and feature extraction. Among the array of pre-processing methodologies utilized, thresholding, median averaging, median filtering, rapid Fourier transform, point operations, intensity modification, and histogram equalization stand out as prominent tools. These techniques are employed to mitigate noise, enhance contrast, and optimize the overall visual quality of the image. Once the pre-processing phase is complete, the focus often shifts to specific tasks, such as identifying objects or features within the image. In the context of analyzing watermelon images, one such task is the detection of watermelon seeds. To accomplish this, the pre-processed image undergoes further refinement through the application of edge detection techniques. Gradient edge detection, isotropic, Canny, and Sobel edge detection are among the methods commonly employed for this purpose. These techniques aim to highlight the edges and contours of objects within the image, facilitating the identification of distinct features such as watermelon seeds. However, our investigation reveals that not all edge detection methods are equally effective in this context. By employing a combination of pre-processing techniques and judiciously selecting edge detection methods, researchers can enhance the accuracy and reliability of their image processing workflows, ultimately advancing our understanding of complex biological structures such as watermelon seeds.
Co-Authors Adilal Mahbub, Luthfi Agnes Rustiana Litianing Tyas Agus Setiawan Gusti Agus Supriatman Ahmad Fauzi, Irgi Ahmad Wakih, Agus Akbar Kasyfurrahman, Muhammad Aries Kusdaryono Arnie Retno Mariana Drajat, Hilmi Maulana Dzulhaq, Muhammad Iqbal Efrida, Ririn Evi Dewi Sri Mulyani Fadilah, Nizar Fauzan Natsir Ginasti, Angga Gusti, Agus Setiawan Hakim, Zainul Hendrawansyah Hendrawansyah Hendrawansyah, Hendrawansyah Hikmatyar, Missi Hilma Lutfiani Ika Nuraeni Iksanudin, Muhammad Nafi Indrawan, Telly Iqbal Fauzy, Fery Iqbal, Muchamad Ismu Arief, Mizar Kusumahdinata, R. Ramdan Mahdalena, Nida Mariana, Arnie Retno Maulana Hidayat, Cecep mubarokah, isna Muchamad Iqbal Muhammad Iqbal Dzulhaq Muhammad Nafi Iksanudin Nida Mahdalena Noor Ekajati, Eky Novi Asniar Nuk Ghurroh Setyoningrum Nur Widiyasono, Nur Nuraeni, Ika Nurhidayat, Wahyu Nusamsi, Dede Rizal Praseptiawan, Mugi Prasetia, Kurdiman Ary Putri Safira R. Arif Malik Ramadhan Rahman Rahman Rahmat Hidayat, Cepi Raka Agung Gumilar Ramadan, Anri Noor Annisa Rasi Nuraeni Ririn Efrida Rismansyah, Riki Roska Rizal, Randi rudi hartono Ruuhwan Ruuhwan Ruuhwan Safira, Putri Shinta Siti Sundari Srie Rezeki Nur Endah Suherman, Nurisya Rahma Susanti Susanti Teuku Mufizar Triono Triono Triono Triono Tyas, Agnes Rustiana Litianing Wahyu Nurhidayat Yusuf Sumaryana ZAFRIEL AGIRALDI CHANDRA Zainul Hakim