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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Scientific Journal of Informatics Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknoinfo Jurnal Sisfokom (Sistem Informasi dan Komputer) Voice Of Informatics Jurnal Informasi dan Komputer METIK JURNAL E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Journal of Information Systems and Informatics Brahmana : Jurnal Penerapan Kecerdasan Buatan Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Pendidikan dan Teknologi Indonesia ADI Pengabdian kepada Masyarakat Jurnal (ADIMAS Jurnal) Jurnal Pengabdian Literasi Digital Indonesia Jurnal Abdimastek (Pengabdian Masyarakat Berbasis Teknologi)
Kirana, Chandra
ISB Atma Luhur
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Social Sciences Other

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Analisis Efisiensi Waktu Bubble, Insertion, Merge, Dan Quick Sort Menggunakan Python Sutanto, Daniel Septhiady; Chandra, Chandra Kirana; Wahyuningsih, Delpiah
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 1 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v9i1.1053

Abstract

Sorting is a fundamental process in data processing that plays an important role in increasing the efficiency of other algorithms. This study aims to conduct a comparative analysis of four classic sorting algorithms: Bubble Sort, Insertion Sort, Merge Sort, and Quick Sort. The comparison is based on three main paRAMeters: Execution time, algorithm Complexity. Testing was carried out experimentally using small to large random Datasets with test scenarios of 100, 150, 300 using the Python progRAMming language. The results showed that Merge Sort is the most efficient algorithm in terms of time because the average time required is 0.000165 seconds.
Implementation of the Complaint Service Application at the Human Resources Bureau Division of the Bangka Belitung District Police Based on Android Pratama, Hafish Mairendra; Kirana, Chandra; Wahyuningsih, Delpiah
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 2 (2025): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i2.906

Abstract

Digital transformation in human resource (HR) management within government institutions requires innovative solutions to enhance efficiency and transparency. The HR Bureau Division of the Bangka Belitung Islands Police (Polda Kep. Bangka Belitung) currently faces challenges with its manual complaint management system, leading to delays in grievance handling. This study aims to develop and implement an Android-based complaint service application to facilitate effective reporting and monitoring of complaints. The system development methodology employs the Software Development Life Cycle (SDLC), encompassing planning, requirement analysis, design, implementation, testing, and maintenance phases. The results indicate that the developed application successfully meets user needs with key features such as complaint submission, complaint history, and user guidelines. Blackbox testing confirms that all functions operate as expected. This application is expected to improve accountability and efficiency in the complaint handling process within the HR Bureau Division, while also supporting e-government policies.
Deteksi Anomali Polusi Udara Menggunakan Algoritma Isolation Forest tanpa Label pada Dataset Kualitas Udara Torino Amanda, Reffi; Helmud, Ellya; Kirana, Chandra
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 9 (2025): JPTI - September 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.950

Abstract

Polusi udara merupakan masalah masalah lingkungan yang berdampak langsung pada Kesehatan dan kualitas hidup manusia. Tujuan dari penelitian ini adalah untuk menggunakan algoritma Isolation Forest berbasis Unsupervised Learning, untuk menemukan anomali dalam data kualitas udara dan mengetahui situasi yang tidak normal dengan cepat dan akurat tanpa memerlukan label. Isolation forest dipilih karena efisien dalam menangani data yang besar dan bekerja dengan cepat dalam ruang fitur tinggi dibandingkan dengan algoritma yang lain. Penelitian ini mengimplementasikan algoritma isolation forest untuk dilakukannya identifikasi outlier pada data kualitas udara, khususnya parameter karbon monoksida (CO), nitrogen dioksida (NO?), nitrogen oksida (Nox), dan benzene (C6H6) dari dataset UCI Air Quality. Penelitian ini dilakukan dengan studi literatur, pengumpulan data, preprocessing (pembersihan data dan penanganan nilai hilang), analisis eksploratif, implementasi algoritma, serta visualisasi hasil. Hasilnya, dari total 9357 data, terdeteksi 468 anomali (5%) dengan karakteristik lonjakan nilai ekstrim seperti CO 8.1 mg/m³ dan NO? 187 µg/m³. Visualisasi grafik temporal dan boxplot memperkuat penelitian ini, dengan menunjukkan distribusi anomali yang tersebar. Sehingga, pendekatan ini bisa digunakan sebagai sistem peringatan dini terhadap lonjakan polusi udara yang berbahaya, sehingga berkontribusi dalam sistem monitoring kualitas udara otomatis yang lebih adaptif dan real-time.
Expert System for Early Childhood Talent Detection Using Certainty Factor and Dempster Shafer Algorithms Supardi, Supardi; Kirana, Chandra; Ferdian, Ferdian
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1283

Abstract

Early life is a crucial window for recognizing children’s interests and talents that shape later development. This study implements and compares two reasoning algorithms—Certainty Factor (CF) and Dempster–Shafer Theory (DST)—within a rule-based expert system designed to determine early-childhood interests and talents. Observable “symptoms” (behavior, preferences, and responses to stimuli) are mapped to potential talents, including linguistic, musical, logical-mathematical, and kinesthetic intelligences. The CF module computes confidence values from expert-assigned belief weights, yielding a single interpretable score per talent; the DST module aggregates evidence while explicitly representing uncertainty through basic probability assignments over the frame of discernment. We evaluate both methods in the deployed application with respect to accuracy, decision consistency, and response speed. Results show that, for the representative trait set aligning with linguistic indicators, CF produced the highest agreement with expert judgment 84% confidence while DST assigned 65% mass to the same singleton hypothesis, reserving the remainder for competing hypotheses and ignorance. These findings indicate that CF offers a more decisive signal under congruent evidence, whereas DST contributes caution by quantifying residual uncertainty. Together, the dual approach supports transparent and scalable screening of early talents, enabling caregivers and educators to act when support is strong and seek additional observations when uncertainty persists.
Co-Authors Abdul Holil Agustina Mardeka Raya Amanda, Reffi Anisah Anisah . Anisah, A Benny Wijaya Burham Isnanto Della Puspita Delpiah Wahyuningsih Devi Irawan Dhita Heru Pambudi Donny Muda Priyangan Edi Sugianto Elly Yanuarti Ellya Helmud ellya helmud, ellya Ferdian, Ferdian Hafiz, Aliy Hamidah . Irawadi, Syafrul Kiswanto Wanto Lukas Tommy M. Indra Wijaya Melati Suci Mayasari, Melati Suci Okkita Rizan Parlia Romadiana Parlia Romadiana Perkasa, Eza Budi Pradana, Harrizki Arie Pradana, Harrizki Arie Pratama, Hafish Mairendra Rahmat Sulaiman Ramanda Salbiyanti Ricky Tjhin Suci Amalia Arfah Sulaiman, Rahmat - Supardi Supardi Supardi Supardi, S Susanto, Ferry Sutanto, Daniel Septhiady Vivi Lindawati Wahdaniyah, Risti Yongkie Setiawan