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All Journal Jurnal Transformatika Jurnal CoreIT Jurnal Informasi dan Komputer Jurnal Esensi Infokom: Jurnal Esensi Sistem Informasi dan Sistem Komputer Jurnal Teknologi Sistem Informasi
Fauzi, Chairani
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Mathematics Other

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PREDIKSI PENDAFTARAN PESERTA DIDIK BARU DENGAN METODE POLYNOMIAL REGRESSION, DAN K-MEDOIDS ., Noviana; Fartesa, Justi; Fauzi, Chairani; ., Sriyanto
Jurnal informasi dan komputer Vol 11 No 02 (2023): Jurnal Informasi dan Komputer yang terbit pada tahun 2023 pada bulan 10 (Oktobe
Publisher : LPPM Institut Teknologi Bisnis Dan Bahasa Dian Cipta Cendikia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35959/jik.v11i02.528

Abstract

One school attempted to predict the acceptance of new students based on data from the previous year, but the results were inaccurate. The fluctuation in the number of new student admissions is a problem for SMK Negeri 2 Kotabumi in preparing class facilities, uniforms, books to support learning activities and determining steps and policies related to school promotion and targets for new student admissions in the following years. Predicting new student enrollment using the polynomial regression method, and K-Medoids, in processing student enrollment prediction data. The results obtained are Y values that are in accordance with the implementation results using python. For example, in 2018 the value Y = 0.0034x6 - 0.6194x5 + 46.754x4 - 1864.6x3 + 41412x2 - 485358x + 2E+06 = 1744.01 with R = 0.8779 accompanied by the same for each year, whereas for the K- Medoids method obtained in 2018 clustering 0 obtained 73 prospective students in the non-passing category and 19 in the pass category, while for 2019 to 2022 the number of cluster 0 is worth 0 and cluster 1 is worth 92 which means that all participants have passed
Perbandingan Simple Additive Weighting dan Weighted Product Pada Penerimaan Bantuan Raskin Albarqi, Achmadi Hudadin; Rosman, Firdaus; Lestari, Sri; Fauzi, Chairani
Jurnal Teknologi Sistem Informasi Vol 5 No 1 (2024): Jurnal Teknologi Sistem Informasi
Publisher : Program Studi Sistem Informasi, Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jtsi.v5i1.6997

Abstract

In an effort to reduce the burden on family budgets, the government implemented the Poor Rice Program (Raskin), a social security program for the poor that offers subsidized rice assistance to low-income households. However, in practice, many households are categorized as poor even though they should not be. On the other hand, poor families should be involved, but are not included, and the process is still seen as less focused. Errors in data processing procedures can occur because Raskin recipients are still determined by human data processing. Data processing is time consuming, especially when it comes to ranking and decision making. In this regard, this research explores ways in which Decision Support Systems (DSS) can encourage faster and more precise decision making. Simple Additive Weighting (SAW) and Weighted Product (WP) are the two main methods covered. To increase the effectiveness and accuracy of government aid programs in Indonesia, the main objective of this research is to compare the use of SAW and WP techniques in making decisions regarding the receipt of Rice Miskin aid. The findings of this research support each other by referring to local residents with the pseudonyms W3, W12, W9 , W11, and W17 as recipients of rice assistance from regional governments who are inadequate in decision-making authority positions. Keywords: Decision Support System(DSS),Raskin, Simple Additive Weighting, Weighted Product.
Pemanfaatan Artificial Neural Network Dengan Teknik Backpropagation Untuk Prakiraan Cuaca Harian Zulfiani, Ayu; Fauzi, Chairani
Jurnal Transformatika Vol 21, No 1 (2023): July 2023
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.6937

Abstract

Berdasarkan data dari Badan Nasional Penanggulangan Bencana (BNPB), selama tahun 2022 saja, telah terjadi 3.054 bencana dengan korban meninggal sampai 392 orang, dengan jumlah kejadian cuaca ekstrim dapat mencapai 931 kejadian.  Untuk mengantisipasi dampak yang ditimbulkan oleh cuaca ekstrim, BMKG mengeluarkan prakiraan cuaca, agar masyarakat siap, ketika cuaca ekstrim itu datang. Aplikasi penggunaan teknik Artificial Neural Network (ANN) pada prakiraan cuaca yang sangat berdampak, meningkatkan kemampuan untuk menyelami luasnya big data dalam mendapatkan informasi yang diperlukan, sebagai pembantu yang tepat bagi prakiraan dan pembuatan kebijakan. Data yang digunakan pada penelitian ini adalah data unsur-unsur cuaca, seperti tekanan, suhu udara, kelembaban, arah dan kecepatan angin, serta curah hujan, yang didapatkan dari Stasiun Meteorologi Radin Inten II Lampung. Data observasi memiliki kerapatan data per 1 jam, dengan rentang waktu selama 5 tahun yaitu dari 01 Januari 2018 – 31 Desember 2022. Metode yang dipakai dalam penelitian ini adalah Backpropagation Neural Network (BPNN). Hasil penelitian menunjukkan BPNN dapat memprakirakan hujan terklasifikasi dengan baik dibandingkan metode lainnya. 
Optimizing Student Depression Prediction Using Particle Swarm Optimization and Random Forest Effendi, Mukhammad Khoirul; -, Sriyanto; Irianto, Suhendro Yusuf; Fauzi, Chairani; Vitriani, Yelfi
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 1 (2025): June 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i1.35954

Abstract

Student mental health is a growing concern due to increasing academic pressure, social demands, and economic factors affecting their well-being. Depression, a common issue among students, significantly impacts academic performance and overall quality of life. Therefore, early detection and accurate prediction of student mental health conditions are essential to provide timely interventions. This study aims to improve the accuracy of depression prediction among university students by integrating Particle Swarm Optimization (PSO) for feature selection with Random Forest (RF) as the classification model. The dataset used is the Student Depression Dataset from Kaggle, consisting of 27,900 respondents with 18 features related to demographic, academic, and psychological factors. Data preprocessing includes handling missing values, normalization, categorical encoding, and feature selection using PSO. The model is trained and evaluated using 10-Fold Cross-Validation. Experimental results show that PSO-optimized Random Forest outperforms the standard Random Forest model. The optimized model achieves an accuracy of 84.08%, precision of 82.79%, recall of 77.79%, and an AUC-ROC score of 0.912, improving classification performance. These findings demonstrate that PSO effectively enhances feature selection, leading to better classification accuracy. This study contributes to the development of a more accurate and efficient machine learning model for detecting student depression. By optimizing feature selection, this approach reduces computational complexity while maintaining high predictive performance. Future research can explore hybrid optimization techniques such as Genetic Algorithm (GA) or Differential Evolution (DE) to further enhance model generalization across different datasets.
Sistem Pendukung Keputusan Penerimaan Bantuan PKH di Kelurahan Tanjung Sari Swandika, I Komang; Albarqi, Achmadi Hudadin; Fauzi, Chairani; Lestari, Sri
Jurnal Esensi Infokom : Jurnal Esensi Sistem Informasi dan Sistem Komputer Vol 8 No 1 (2024): Jurnal Esensi Infokom : Jurnal esensi sistem informasi dan sistem komputer
Publisher : Lembaga Riset dan Pengabdian Masyarakat Institut Bisnis Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55886/infokom.v8i1.819

Abstract

Berdasarkan temuan berbagai penelitian, terdapat banyak kesalahan dalam distribusi statistik Program Keluarga Harapan (PKH) yang tidak akurat. Hal serupa juga ditunjukkan oleh hasil survei yang dilakukan di Kelurahan Tanjung Sari , Buay Pemaca, Kab. Ogan Komering Ulu Selatan . Hal ini menunjukkan bahwa masih banyak masyarakat yang masih mempunyai klaim atas uang tersebut namun tidak menerimanya. Terutama jika sejumlah calon peserta berada dalam kondisi miskin atau kurang beruntung dan tingkat kelayakan mereka hampir sama.Penelitian Penerimaan Program Keluarga Harapan (PKH) dengan Memanfaatkan Metodologi Simple Additive Weighting (SAW) dan Weighted Product (WP) pada Sistem Pendukung Keputusan (SPK) PKH di Kelurahan Tanjung Sari, Buay Pemaca .Hasil pada penelitian disini menunjukkan bahwa meskipun terdapat perbedaan di antara masing-masing pendekatan, seperti yang ditunjukkan oleh perbandingan hasil pemeringkatan pada Tabel 9, terdapat kesamaan antara hasil dari Peringkat 1–12 dan perbedaan antara hasil dari Peringkat 13–27.Penelitian menyimpulkan bahwa di Kelurahan Tanjung Sari , Buay Pemaca, , metode Weight Product (WP) dapat direkomendasikan sebagai metode sistem pendukung keputusan penerimaan bantuan PKH. Hasil yang ditampilkan dalam penelitian ini mempunyai rentang nilai yang sangat sempit, menandakan bahwa keakuratan data telah teruji.
Co-Authors ., Noviana Albarqi, Achmadi Hudadin Effendi, Mukhammad Khoirul Fartesa, Justi Firdaus Rosman, Firdaus Sri Lestari Sriyanto . Sriyanto Sriyanto Swandika, I Komang Vitriani, Yelfi Y. Suhendro Zulfiani, Ayu