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Classification of Remission Data for Prisoners in Tangerang Class IIA Correctional Institution using K-Nearest Neighbor Algorithm Abdillah, Refa Maulana; Agung, Halim; Ramdhan, Syaipul
G-Tech: Jurnal Teknologi Terapan Vol 9 No 3 (2025): G-Tech, Vol. 9 No. 3 July 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i3.7532

Abstract

The classification of remission eligibility for prisoners is a critical issue in correctional institutions, as it directly impacts prison management and the rehabilitation process. Special remission is a reduction of sentence granted to prisoners based on specific criteria, including religious status and the type of remission granted. This research aims to address the challenge of classifying special remission data for prisoners at the Class IIA Tangerang Correctional Facility using the K-Nearest Neighbor (KNN) algorithm. The dataset used in this study includes four indicators: Length of Sentence, Remaining Sentence, Crime Type, and Risk Dimension, which are analyzed to predict the remission status to be granted. The KNN model, with a parameter of k=1, achieved an accuracy of 93.94%. However, the model struggled to accurately classify the "No Remission" class, resulting in failures to detect prisoners who are not eligible for remission. The data processing steps included converting categorical data into numerical format, data normalization, and splitting the data into training and testing sets. Model evaluation was conducted using Confusion Matrix, Precision, Recall, and F1-Score. The findings suggest that while the KNN algorithm can be effectively used to classify remission status, further improvements are needed to address class imbalance and optimize results.
Perbandingan Klasifikasi Tipe Kesuksesan Generasi Z Menggunakan Algoritma Naïve Bayes dan Decision Tree Novara Aulist Zakia; Ryando, M. Bucci; Agung, Halim
TEMATIK Vol. 12 No. 1 (2025): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2025
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v12i1.2334

Abstract

This study aims to classify the types of success of Generation Z using the CRISP-DM method approach and using the Naïve Bayes and Decision Tree algorithms. Generation Z who grew up in a digital environment has a unique view of the meaning of success, which is no longer limited to income or position, but also includes life balance and self-development. This study identifies several important factors such as educational background, technological skills, work experience, personal branding, and use of social media as determining variables in the classification of types of success. The classification model produces four main categories of success, namely financial, career, self-development, and life balance. The results showed that life balance was the most dominant category of success among respondents. The use of the Naïve Bayes and Decision Tree algorithms showed that Decision Tree with balancing techniques (random oversampling) provided the highest classification accuracy, which was 94%, compared to Naïve Bayes which only reached 37%. This study makes an important contribution to the development of human resource strategies, education, and policies that are relevant to the characteristics and aspirations of Generation Z in the digital era.
Bahasa Inggris Bahasa Inggris Handoko, Maulana Bhakti; Fauzi, Zidan Wahyu; Agung, Halim; Sofia, Detin
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2577

Abstract

Selecting a major in Senior High School significantly shapes students’ academic paths and future careers. However, the current process often lacks objectivity, relying on subjective teacher consultations and academic data without standardized analysis. This study addresses this gap by developing a decision support system using the Decision Tree algorithm to assist students at Al-Istiqomah High School in choosing between science and social science majors, based on academic performance and non-academic factors like attitudes and attendance. The study follows the CRISP-DM methodology, which includes six stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Academic records from 123 students were used to build the model. The Decision Tree algorithm identified mathematics, biology, and physics scores as key predictors for major classification. The model demonstrated high predictive performance, with 96% accuracy, 100% precision, and 95% recall. Additionally, an Area Under the Curve (AUC) of 97% confirmed the model’s robust ability to distinguish between science and social science tracks. This system was implemented as a user-friendly web application using Streamlit, enabling students and educators to input data and receive immediate major predictions. By offering objective, data-driven recommendations, the system helps students make more informed decisions about their academic futures and provides educators with targeted, evidence-based advice. These results highlight the Decision Tree algorithm as an effective, efficient, and practical tool for enhancing the academic advising process and supporting students in selecting the major that best fits their strengths and interests.
Rancangan Overlay untuk Meningkatkan Kualitas Tampilan Konten Live Shopping pada Marketplace Agung, Halim; Sukrajap, Muhamad Ali; Badri, Wahid Maulana
JURNAL TREN BISNIS GLOBAL Vol 5, No 2 (2025): JURNAL TREN BISNIS GLOBAL
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/jtbg.v5i2.16092

Abstract

Di era globalisasi saat ini, Tiktok menjadi salah satu platform media sosial yang paling populer. Dengan berkembangnya Tiktok Live Shopping, persaingan di antara penjual yang menggunakan live streaming juga semakin meningkat. Oleh sebab itu diperlukan strategi yang lebih canggih untuk menonjolkan diri di tengah keramaian. Salah satu strategi untuk meningkatkan kualitas tampilan konten ialah dengan desain overlay yang lebih menarik dan profesional. Penelitian ini memiliki tujuan untuk merancang desain yang membantu pengguna Shopee dalam menerapkan overlay yang menarik dan interaktif untuk live streaming mereka. Penelitian ini menggunakan metode studi pustaka, observasi serta kuantitatif. Hasil penelitian ini menunjukkan bahwa desain dapat di mulai dengan mendesain background yang pas dengan tema barang yang akan di jual serta penerapan overlay dapat dilakukan dengan bantuan OBS dimana desain, host dan audio di masukan ke dalam aplikasi ini. Berdasarkan pengolahan data evaluasi secara keseluruhan bahwa dari 55 responden berhasil mendapatkan score 55% poin, dengan demikian desain Overlay dapat dikatakan cukup untuk dapat dijadikan dan di terapkan dalam Live Shopping.
Co-Authors Abdillah, Refa Maulana Abiel Filetus Agung Stefanus Kembau Alvin Juvianto Alvin Juvianto, Alvin Amanda, Mercedes Andreas, Andri Axell Thomas Badri, Wahid Maulana Brigitta Indrawan, Genoveva Budiman Budiman Candra, Ricky Chandra Arief Rahardja Charles Widodo Christanti Christanti CHRISTANTI CHRISTANTI, CHRISTANTI Christian, Michael Christine Christine Christine Citra, Calvin Christopher Claude Calvin Alsher Danny Steveson Edy Andersen Erlando Aubrey Nathaniel Evasaria Magdalena Sipayung, Evasaria Magdalena Farady Marta, Rustono Fauzi, Zidan Wahyu Fendra Mulia Firellsya, Gabriela Fitri Murfianti Hakim, Lukman Handoko, Maulana Bhakti Hariyanto, Ribfan Harvin Seruni I Gede Wisnu Satria Chandra Putra Ivan Ivan Ivan Ivan Januar, Rizky Jeki Sauwani Jessica Jessica Jessica Jessica Jimmy Gunawan Johanes Fernandes Andry Juardi, Try Juniana, Paula Karlo Geofandy Karman Surya Kenny Kevin Ekaputra Kevin Krisnandi Kevin Laurence Hartono Lieta Septiarysa Linda Linda Lukman Hakim M. Bucci Ryando Marchellius Yana Mario Richie Marlina Marlina Maynacky Muhamad Ali Sukrajap Nico Yunus Marselinus Novara Aulist Zakia Oscar Adriyanto Phoan, Nico Prittania Friska Octavia Rahardja, Chandra Arief Rainhard Rainhard Ramdhan, Syaipul Raphael, Stephanus Rarasati, Dionisia Bhisetya Richard Benedict Ricky Candra Ricky Raymond Ricky Ricky Rini Lestari Rizky Januar Rustono - Rustono Farady Marta Rustono Farady Marta Sandi Gautama, Sandi Septiarysa, Lieta Setiawan, Vincent Sinata, Frans SOFIA, DETIN Stefanus Kembau, Agung Stephanus Raphael Steven Steven Andreas Teady Matius Surya Mulyana, Teady Matius Thomas, Axell Tobing, Fery Tri Yatmo Noveary Try Juardi Valerian Hendri Yono Venness, Venness Vicky Budiman Vina Virshella Vitcky Nanda Putra Wang, Changsong Wilibrodus Wilibrodus Willyam Willyam Wiryadinata, Antonius Yessica Putri Santoso Yogi Swara Hendro Leksmono Yogyawan Yogyawan Yohanes Dianrizkita Yosephine, Maria Yuliana Yuliana