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KUSTIAN, NUNU
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Implementasi Metode Multi Attribute Utility Theory (MAUT) Dalam Pemilihan Mobil Bekas Pada Showroom Mobil Dagangan Cakung Jakarta Timur Saputra, Sendy Alfigo Yoga; Kustian, Nunu; Pramarta, Pandhu
Jurnal Ilmu Komputer dan Bisnis Vol. 15 No. 2a (2024): Vol. 15 No. 2a Special Issue (2024)
Publisher : STMIK Dharmapala Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47927/jikb.v15i2a.905

Abstract

Penelitian ini bertujuan mengembangkan sebuah alat bantu keputusan yang dapat membantu pemilik Showroom Mobil Dagangan Cakung Jakarta Timur dalam memilih mobil bekas secara optimal. Metode Multi Attribute Utility Theory (MAUT) diterapkan dalam SPK ini untuk mengevaluasi berbagai alternatif mobil bekas berdasarkan sejumlah kriteria, seperti pajak mobil, kondisi interior, kondisi eksterior, kondisi mesin, dan kelengkapan dokumen. Data yang digunakan dengan mengumpulkan dari dealer mobil, penjualan mobil secara personal, dan situs web penjualan. Indikasi penelitian menyatakan bahwa metode MAUT efektif dalam memberikan rekomendasi yang sepadan dengan pemilik showroom, dan membantu mengurangi risiko kesalahan dalam penentuan memilih mobil bekas.  
Implementasi Metode SAW Pada Aplikasi Akademik dan Seleksi Siswa Terbaik di SMK Negeri 19 Jakarta Pusat Kustian, Nunu; Ridwan, Rizki
Jurnal Ilmu Komputer dan Bisnis Vol. 16 No. 1 (2025): Vol. 16 No. 1 Mei (2025)
Publisher : STMIK Dharmapala Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47927/jikb.v16i1.952

Abstract

Proses pemilihan siswa terbaik di lingkungan Pendidikan sering kali membutuhkan waktu dan tenaga, terutama jika dilakukan secara manual. Penelitian ini bertujuan untuk mengembangkan aplikasi berbasis metode Simple Additive Weighting (SAW ) guna mempermudah seleksi siswa terbaik di SMK Negeri 19 Jakarta Pusat. Metode SAW dipilih karena keunggulannya dalam mengolah data multi-kriteria secara efisien dan sistematis. Aplikasi  ini memadukan berbagai indikator penilaian, seperti nilai akademik, kehadiran, dan aktivitas ekstrakurikuler, untuk memberikan hasil yang lebih objektif. Hasil implementasi menunjukkan bahwa aplikasi ini mampu mempercepat proses pengolahan data sekaligus meningkatkan akurasi dalam pemilihan siswa terbaik. Selain itu, fitur laporan otomatis dan antarmuka yang mudah digunakan membantu staf Pendidikan mengelola data dengan lebih praktis.
Analysis of Attendance and Parental Occupation Data to Predict Student Dropout Risk Using Decision Trees Kustian, Nunu; Julaeha, Siti; Khotimah, Khusnul
International Journal of Business and Information Technology Vol. 6 No. 2 (2025): December
Publisher : LPPM STMIK Dharmapala Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47927/ijobit.v6i2.1345

Abstract

Student dropout continues to pose a critical challenge that hinders educational equality and long term human capital development. This study aims to predict dropout risk by employing attendance records and parental occupation as the main indicators. A synthetic dataset reflecting Indonesian school conditions was analyzed using the Decision Tree algorithm. The results demonstrate that the model achieved strong predictive capability, reaching 87% accuracy with well balanced precision and recall values. Further analysis highlights absenteeism as the most decisive factor influencing dropout risk. In addition, parental occupation emerges as a contextual determinant that strengthens risk identification, with students whose parents are engaged in informal or unstable sectors being more vulnerable compared to peers from households with stable formal employment. The transparent structure of the Decision Tree enhances its practical value for educational practitioners, as it translates complex data into insights that are both actionable and accessible. While the findings are based on simulated data, the study underscores the importance of integrating behavioral and socioeconomic indicators into early detection frameworks for student dropout.
Virtual Learning Berbasis Karakter Virtual Pada SDN Jatimekar I Bekasi Julaeha, Siti; Kustian, Nunu; Ambarsari, Erlin Windia
Kapas: Kumpulan Artikel Pengabdian Masyarakat Vol 4, No 2 (2025)
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/ks.v4i2.4441

Abstract

Pembelajaran daring terus berkembang dengan inovasi teknologi, salah satunya melalui penggunaan karakter virtual sebagai media interaktif. Pelatihan ini mengarah pada penerapan virtual learning berbasis karakter virtual di SDN Jatimekar I Bekasi dengan memanfaatkan aplikasi Vroid Studio, 3Tene, dan OBS Studio sebagai alat bantu mengajar bagi guru. Metode yang digunakan meliputi tahap kegiatan, persiapan, dan pelaksanaan. Guru diberikan pelatihan untuk membuat dan mengoperasikan karakter visual yang bergerak secara real-time melalui webcam dan mikrofon, sehingga dapat menampilkan ekspresi wajah dan sinkronisasi suara guna meningkatkan keterlibatan siswa. Hasil implementasi pengabdian masyarakat menggambarkan bahwa penggunaan karakter virtual meningkatkan minat dan partisipasi siswa serta membantu guru menyampaikan materi secara lebih menarik dan responsif. Virtual learning berbasis karakter virtual berpotensi meningkatkan efektivitas pembelajaran daring dan dapat menjadi solusi inovatif dalam Pendidikan Dasar dengan dukungan teknologi dan pelatihan yang memadai.
SISTEM APLIKASI ADMINISTRASI PENDATAAN PENDUDUK RT.003 RW.001 CILIWUNG KEMIRIMUKA KOTA DEPOK Muhamad Salman Alfarisi; Nunu Kustian
Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 2 No. 3 (2022): November : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/teknik.v2i3.523

Abstract

Neighborhood association is an organizing structure in which the Indonesian government recognizes and fosters to assist administrative tasks in building communities at the village and sub-district levels. But on RT. 003 RW. 001 Ciliwung Kemirimuka Depok City there are various problems in the management of population data and its services which consist of traditional filings into a general ledger used as physical archives so that they are vulnerable to damage and often loss of population data and cause inaccuracies in the number of population data who move, experiencing death, and residents who came. The purpose of this study is to build a population data administration system into an integrated application whose data can be stored into a database to help the service process that can be managed easily. The method used by the Research and Development (R&D) method to create products is in the form of an administrative application for RT.003 RW.001 Ciliwung Kemirimuka Depok City population data collection. The results of this study can be used for population data collection applications by RT.003 RW.001 officers as a user so it does not require more time and data search can be done easily.
Evaluasi Kinerja Algoritma Naïve Bayes untuk Diagnosis Kanker Paru Berbasis Data Klinis Terstruktur Nunu Kustian; Putri Dina Mardika; Reko Syarif Hidayatullah
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 2 (2026): Juli : Jurnal Informatika dan Tekonologi Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i2.11077

Abstract

The rising incidence of lung cancer is often outpaced by the constraints of medical resources, creating a critical gap that frequently leads to diagnostic delays and hindered treatment interventions. Early-stage detection is therefore fundamentally important to boost therapy efficacy and extend patient survival rates. This study proposes a computational diagnostic model utilizing the Naïve Bayes algorithm to classify lung cancer status based on structured clinical datasets encompassing demographic profiles, smoking behaviors, and patient reported symptoms. Data integrity was prioritized through rigorous preprocessing, which included handling missing values and transforming categorical variables into a numerical format suitable for probabilistic modeling. The Naïve Bayes framework was chosen for its interpretability, robustness, and efficiency in handling tabular clinical data. Empirical evaluations yielded highly promising results, with the proposed model achieving an accuracy of 95%, precision of 98%, recall of 96%, and an F1-score of 97%. These findings validate the capability of the algorithm to provide dependable diagnostic predictions, demonstrating its potential as a decision support tool to streamline early screening processes for lung cancer.  
Co-Authors Aan Risdiana Adha, Muhammad Faisal Agus Darmawan Anugrahni, Asih CLEOPATRA, MARIA Dudi Parulian Ek Ajeng Rahmi Pinahayu Erlin Windia Ambarsari Fathudin, Dedin Fathur, Aji Frasdica Fujianto Nugraha Hidayatullah, Reko Syarif Irfan Ismunandar JUHRIAH, EEN khusnul khotimah Mardiana Mardiana Mochamad Rizki Adityo mufti, abdul Muhamad Alfarisi Muhammad Tri Habibie Ningsih, Retna Nirwana, Bunga Niswati, Za’imatun Parulian, Dudi Pinahayu, Ek Ajeng Pramarta, Pandhu Prayogo, Rachmad Danu Putri Dina Mardika Rahayu, Wanti Reko Syarif Hidayatullah Retna Ningsih Ridwan, Rizki Risma Nurul Auliya Rizki Ridwan Rizki Ridwan Rudi Prasetya Saputra, Sendy Alfigo Yoga Saputro, Bambang Adi Siti Julaeha Siti Julaeha Siti Julaeha, Siti Sonny, Michael Syafrudin, Nita Najiah Syahputra, Fadel Maulana Triyono, Anugrah Hendri Wanti Rahayu Wanti Rahayu Wulan Anggraeni