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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer AMMA : Jurnal Pengabdian Masyarakat
Fadhil Muhammad Basysyar
STMIK IKMI Cirebon
Humanities Chemistry Computer Science & IT Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing

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Pengelompokan Penjualan Madu Menggunakan Algoritma K-Means Danya Rizki Chaerunisa; Nining Rahaningsih; Fadhil Muhammad Basysyar; Ade Irma Purnamasari; Nana Suarna
KOPERTIP : Jurnal Ilmiah Manajemen Informatika dan Komputer Vol. 5 No. 1 (2021): KOPERTIP : Jurnal Ilmiah Manajemen Informatika dan Komputer
Publisher : Puslitbang Kopertip Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32485/kopertip.v5i1.144

Abstract

Kekayan alam Indonesia memang sudah tidak diragukan lagi, keberadaan hutan di Indonesia dengan luas lahan sekitar 195 juta hektar berpotensi untuk pembudidayaan madu. Madu merupakan salah satu produk hasil hutan bukan kayu. Di Indonesia sendiri dengan luasnya lahan, berpotensi menjadi pakan lebah madu. Selain dikonsumsi sebagai madu murni, madu juga dimanfaatkan sebagai pencampur makanan, kecantikan dan obat tradisional. Hal ini membuat peluang bisnis madu di Indonesia menjadi tinggi. Peluang ini dimanfaatkan oleh toko madu bernama Teras Palawi dibawah naungan Perum Perhutani Kota Cirebon. Dengan meningkatnya angka covid-19 di Indonesia menjadikan penjualan madu ini meningkat drastis, selain rasanya yang manis dengan berbagai macam rasa seperti kelengkeng, bunga liar dan randu berbagai khasiat yang ada pada madu menjadikan madu ini digemari oleh semua kalangan dari anak kecil hingga orang dewasa- serta dapat memberitahu perusahaan, jenis madu mana yang banyak diminati. Dalam hal ini dibutuhkan teknik pemanfaatan data menjadi informasi baru atau disebut juga data mining. Metode yang tepat untuk pengelompokan data penjualan madu adalah K-Means karena dalam metode ini dapat mengolah data tanpa diketahui labelnya. Tujuan penulisan tugas akhir ini adalah untuk mengetahui penerapan algoritma K-Means dan untuk mengetahui hasil K Optimum dalam penerapannya menggunakan aplikasi RapidMiner. Hasil uji coba menunjukan adanya enam kelompok sesuai dengan data penjualan madu, dengan K Optimumnya 6 dan hasil davies bouldin index sebesar -0.365 yang dinilai cukup baik karena mendekati angka nol.
Edukasi Harmoni Pasca Pemilu Desa Kertasura Dan Penyuluhan Pencegahan Perpecahan Melalui Animasi Fadhil Muhammad Basysyar; Edi Tohidi; Fajrina Putri Salsabila; Adi Kaswadi; Eddiwan Dwiguna
AMMA : Jurnal Pengabdian Masyarakat Vol. 2 No. 8 : September (2023): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Elections are important moments in a democratic country, but they can often trigger conflict and division in society. Likewise, social media, such as YouTube, has played an increasingly large role in disseminating political information. Counseling on preventing post-election divisions via YouTube in Kertasura Village is a community service initiative that aims to overcome potential post-election social conflicts. In this situation analysis, we will explore partner locations and cases that have occurred, analyze social and cultural aspects in Kertasura Village. The results of community service regarding socialization media videos to prevent post-election divisions based on explainer animations can increase the sense of tolerance and respect for differences of opinion among millennials. Producing socialization media videos based on explainer animations as a socialization media innovation to prevent division after the election that is acceptable among millennials.
ANALYSIS STUDENT EMOTIONS AND MENTAL HEALTH ON CUMULATIVE GPA USING MACHINE LEARNING AND SMOTE Fadhil Muhammad Basysyar; Gifthera Dwilestari; Ade Irma Purnamasari
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5967

Abstract

This research investigates the impact of emotions and mental health on students' cumulative grade point average (CGPA) using machine learning classification algorithms while addressing data imbalances with the Synthetic Minority Oversampling Technique (SMOTE). Emotional well-being and mental health are acknowledged as vital determinants of academic achievement. Data imbalance, particularly in mental health metrics such as anxiety and depression, frequently compromises forecast accuracy. This study improves the accuracy of CGPA prediction based on emotional and mental health factors by utilizing SMOTE in machine learning models such as logistic regression and random forest. A dataset including 226 university students, including academic records and self-reported mental health evaluations, was evaluated. The random forest model attained an accuracy of 87.63%, exceeding the logistic regression model's accuracy of 86.56%. These findings emphasize the significant role of emotions and mental health in academic outcomes and validate SMOTE’s efficacy in addressing class imbalance. This work offers a fresh technique in educational data mining by revealing the possibility for improved academic achievement forecasts based on psychological characteristics, helping to the development of targeted therapies for students experiencing emotional issues. Implications for educational policy emphasize the significance of mental health support systems in promoting academic achievement. Subsequent research should investigate supplementary psychological variables and comprehensible models to improve predictive accuracy and facilitate evidence-based policymaking.
Co-Authors Ade Irma Purnamasari Adi Kaswadi Danya Rizki Chaerunisa Eddiwan Dwiguna Edi Tohidi Fajrina Putri Salsabila Gifthera Dwilestari Nana Suarna Nining Rahaningsih