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All Journal ILKOM Jurnal Ilmiah Jurnal Puan Indonesia Jurnal Desain dan Analisis Teknologi
Sasa Ani Arnomo
Institut Teknologi Batam
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health

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Implementasi Data Intelligence Pada Proses Pengambilan Keputusan Bisnis: (Studi Kasus: Rekomendasi Kontrak Kerja PT.BATM) Saut Pintubipar Saragih; Alice Erni Husein; Sasa Ani Arnomo; Andi Maslan
Jurnal Desain Dan Analisis Teknologi Vol. 5 No. 1 (2026): Januari
Publisher : Aptikom Kepri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58520/jddat.v5i1.97

Abstract

Penelitian ini bertujuan untuk menganalisis data karyawan IT dalam rangka mendukung pengambilan keputusan terkait perpanjangan kontrak kerja. Dataset yang digunakan mencakup data karyawan IT selama periode enam tahun dengan 19 atribut utama, termasuk latar belakang pendidikan, jabatan, durasi kontrak, dan status kepegawaian. Metode penelitian dilakukan melalui tahapan analisis data intelligence yang meliputi proses filterisasi, pembersihan data, serta analisis deskriptif dan korelasional. Hasil penelitian menunjukkan bahwa mayoritas karyawan IT memiliki latar belakang pendidikan sarjana (S1), yang mencerminkan standar rekrutmen yang relatif tinggi. Distribusi durasi kontrak didominasi oleh rentang 7–12 bulan, dengan tingkat keberhasilan probation yang dapat diidentifikasi melalui perbandingan status lulus dan diperpanjang terhadap tidak lulus. Korelasi positif yang kuat (0,65) antara kesesuaian pendidikan IT dan durasi kontrak mengindikasikan bahwa latar belakang pendidikan berpengaruh terhadap retensi karyawan. Dari sisi jabatan, peran senior seperti Project Manager memiliki tingkat retensi tertinggi, sementara peran developer menunjukkan durasi kontrak yang konsisten. Penelitian ini juga menemukan bahwa sekitar 60% resign terjadi dalam enam bulan pertama masa kerja, sehingga bulan ke-3 dan ke-6 diidentifikasi sebagai waktu optimal untuk intervensi retensi.
PELATIHAN ETIKA DAN KOMUNIKASI DIGITAL UNTUK REMAJA DI PONDOK PESANTREN SYAMSUL HUDA BATAM Hendri Kremer; Arwin Ramly; Sasa Ani Arnomo
PUAN INDONESIA Vol. 8 No. 1 (2026): Jurnal PUAN Indonesia Vol. 8 No. 1 Juli 2026
Publisher : ASOSIASI IDEBAHASA KEPRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37296/jpi.v8i1.558

Abstract

The increasing popularity of social networking applications among the younger generation raises crucial issues regarding digital ethics and responsible communicative behavior, educational efforts are needed through various educational channels. This community service program is designed to strengthen the understanding and ability of teenagers in managing social media platforms ethically and responsibly through a specific training program implemented at the Syamsul Huda Islamic Boarding School in Batam. This activity involved 25 school participants with a participatory method consisting of six phases, namely: counseling, interaction-based training, digital content workshop sessions, group forums, practical sessions, and assessments, complemented by pre-test and post-test measurements to evaluate the program's success. Evaluation data shows a 78% increase in understanding of digital ethics and a 65% increase in communication skills through social media platforms, while the level of participant satisfaction with the program reached 92% based on the results of a questionnaire conducted after the training. This training program also plays an important role in improving the quality of social media applications among teenagers and helping them develop wiser attitudes in digital interactions, in line with the initiative to form a digital generation that has ethical integrity and is able to communicate constructively.
Comparative Analysis of Naïve Bayes Variants for Best-Seller Status Determination in the Air Conditioning Industry Sasa Ani Arnomo; Zada Alzena; Heri Nuryanto; Siti Fairuz Nurr Sadikan
ILKOM Jurnal Ilmiah Vol 18, No 2 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i2.3509.393-403

Abstract

The determination of product status such as the best seller predicate for Air Conditioning products is a fundamental sales promotion strategy. Leveraging machine learning to analyze sales data is essential for maximizing business progress and market positioning within the modern electronics industry. This study aims to evaluate and compare the performance and accuracy of Naïve Bayes (NB) classification models in determining AC product status. The goal is to identify the most effective variant among BernoulliNB, GaussianNB, and MultinomialNB for this specific application. A quantitative comparative analysis was conducted using various data record sizes. The primary features analyzed included brand, inverter type, electrical power cooling capacity, address, price, and sales status. In the first scenario, testing across varying data record sizes revealed that the MultinomialNB variant achieved the highest average accuracy at 85.39 percent. In the second scenario, using the full dataset with an 80% training data split without cross-validation, the Naïve Bayes algorithm as a whole demonstrated robust classification ability, reaching a peak prediction accuracy of 99.312 percent. This peak performance significantly outperformed alternative methods such as SVM and KNeighbors Classifier within those specified parameters. This performance significantly outperformed alternative methods such as SVM and KNeighbors Classifier within the specified parameters. The study concludes that the Naïve Bayes model is highly effective for product status classification in the electronics industry. The MultinomialNB variant is identified as the most consistently reliable model for these specific datasets. Future research should consider incorporating cross-validation techniques to further validate model stability across more diverse data environments.
Co-Authors Alice Erni Husein Andi Maslan Arwin Ramly Hendri Kremer Heri Nuryanto Saut Pintubipar Saragih Siti Fairuz Nurr Sadikan Zada Alzena