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All Journal JOIN (Jurnal Online Informatika) Jurnal Mirai Management JOIV : International Journal on Informatics Visualization JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Syntax Literate: Jurnal Ilmiah Indonesia Jurnal Teknoinfo JSiI (Jurnal Sistem Informasi) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) J-Dinamika: Jurnal Pengabdian Kepada Masyarakat IJIES (International Journal of Innovation in Enterprise System) Jurnal Teknologi Informasi dan Pendidikan Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Indonesia : Manajemen Informatika dan Komunikasi JURNAL REKAYASA INFORMASI SWADHARMA (JRIS) Prosiding Konferensi Nasional PKM-CSR eProceedings of Engineering Jurnal INFOTEL Journal of Community Service and Society Empowerment Jurnal Indonesia : Manajemen Informatika dan Komunikasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) The Indonesian Journal of Computer Science SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan
Faqih Hamami
Telkom University, Bandung
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Nursing Public Health Social Sciences Other

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Journal : SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan

 1 
COMPARISON ACCURACY OF C4.5 ALGORITHM AND K-NEAREST NEIGHBORS FOR RAINFALL CLASSIFICATION Muhammad Fauzan Nasrullah; RD. Rohmat Saedudin; Faqih Hamami
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 1 No. 2 (2024): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.14715070

Abstract

Indonesia has a predominantly tropical climate, hence Indonesia experiences limited temperature variations, but has diverse rainfall variations. The variability of rainfall is also inseparable from the impact it has on various aspects of human life and business activities. Therefore, rainfall information is an important aspect in decision making. However, of course, there are stages and methods needed to carry out the analysis process. Therefore, this study looked for the best method between C4.5 and K-Nearest Neighbors which included algorithms in data mining to classify rainfall data. Both algorithms are used to build classification models based on relevant attribute attributes. Then, testing and evaluating both models using various metrics such as Accuracy, Precision, Recall and F1-Score were carried out. In this study also applied Hyperparameter Tuning with the RandomizeSearchCV method to get the best parameters to get maximum accuracy values. The results showed good accuracy values for both algorithms, in the sense that both algorithms were able to classify rainfall based on Indonesia's climate well. Based on the accuracy values obtained with the default parameters of both algorithms, C4.5 produces a higher accuracy value of 81.42%, while K-Nearest Neighbors is only 78.10%. However, after using the best parameters resulting from the application of RandomizedSearchCV Hyperparameter Tuning, a significant change in accuracy value occurred in K-Nearest Neighbors which was found to be 83.37%, while C4.5 increased to 82.56%.
COMPARISON ANALYSIS OF RANDOM FOREST AND NAÏVE BAYES ALGORITHMS FORRAINFALL CLASSIFICATION BASED ON CLIMATE IN INDONESIA Nicolaus Advendea Prakoso Indaryono; RD. Rohmat Saedudin; Faqih Hamami
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 1 No. 2 (2024): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.14715081

Abstract

Indonesia predominantly features a tropical climate across its entirety. With this mostly tropical climate, the country encounters minimal shifts in temperature but exhibits a wide array of rainfall variations. Rainfall patterns in Indonesia showcase significant diversity. These variations in rainfall hold substantial importance in mitigating risks linked to heavy rainfall, such as floods and landslides. Moreover, besides its role in disaster preparedness, rainfall data also holds practical value in sectors such as agriculture, transportation, and industry. By incorporating data mining classification techniques, the process of predicting rainfall in Indonesia can be greatly enhanced. In this study, daily climate data from Indonesia is harnessed, and the chosen method for classification is the random forest algorithm. This selection stems from its capability to generate accurate and consistent classification models without necessitating intricate adjustments of parameters. Furthermore, the Naïve Bayes method is also integrated due to its straightforward implementation and its capacity for simple probability modeling, which can be effectively applied across diverse classification data. The outcomes of this investigation suggest that the random forest algorithm surpasses the Naïve Bayes algorithm in terms of performance and accuracy when classifying climate datasets unique to Indonesia. The random forest algorithm attains an accuracy rate of 86.55%, whereas the Naïve Bayes algorithm lags at an accuracy rate of 36.61%. It is anticipated that these research findings can serve as a point of reference for subsequent scholarly inquiries and contribute to the ongoing monitoring of daily rainfall in Indonesia, thereby aiding in the prevention of natural disasters.
Co-Authors Agus Maolana Hidayat Ahmad, Mokhtarrudin Al amudi, Farhan Hasan Aldi Akbar Ambarita, Ruth Sesilya Anis Farihan Mat Raffei Arrahmani, Farras Hilmy Aziz, Abdurrahman Azzam Imaduddin, Muhammad Budi Rustandi Kartawinata Dahlan, Iqbal Ahmad Deandra, Valen Deden Witarsyah Dimas Raihan Zein Dina Meliana Saragi Fa'rifah, Riska Yanu Fabrianti Kusumasari, Tien Fadhil Hidayat Faishal Mufied Al Anshary Febrianti, Ferda Ayu Dwi Putri Ferda Ayu Dwi Putri Febrianti Ferda Ernawan Fetty Fitriyanti Lubis Firzania, Heidea Yulia Fitri Bimantoro Hadwirianto, Muhammad Raihan Helmayanti, Sheva Aditya I Gede Pasek Suta Wijaya Ilma Nur Hidayati Iqbal Santosa Irfan Darmawan Ismail, Mohd Arfian Jauhari, M.Habib Joel Rayapoh Damanik Kardila, Yuni Kurniawan, Muhammad Rayhan Kuswandi, Brillian Adhiyaksa Lubis, Rizki Aulia Akbar Mangsor, Miza Mardika, Jody Mat Raffei, Anis Farihan Maulana, Fakhri Hassan Muhammad Bryan Gutomo Putra Muhammad Fahmi Hidayat, Muhammad Fahmi Muhammad Fauzan Nasrullah Muhammad Hafizh Murahartawaty Murahartawaty Nasrullah, Muhammad Fauzan Nicolaus Advendea Prakoso Indaryono Novanza, Alvin Renaldy Nuraliza, Hilda Nurul Hidayati Nuryatno, Edi Oktariani Nurul Pratiwi Orvalamarva Pratiwi, Oktaria Nurul Puruhita, Maretha Fitrie Puspitasari, Aprilia Mega Rachmadita Andreswari Raffei, Anis Farihan Mat Rahmah, Najma Syarifa Rahmat Fauzi Ramdani, Dwi Fickri Insan Ramli, Muhammad Ayyub Razali, Raja Razana Raja Rd. Rohmat Saedudin Salsabila Riswanti, Khairunnisa Satya Nugraha, Gibran Sheva Aditya Helmayanti Silmy Sephia Nurashila Sinung Suakanto Suhono Harso Supangkat Sujak, Aznul Fazrin bin Abu Syfani Alya Fauziyyah Tatang Mulyana Tien Fabrianti Kusumasari Vina Fadillah Widyadhari, Dinda Putri Yanu Fa'Rifah, Riska Yudo Husodo, Ario Yulizar, Iqbal Zahid, Azham