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All Journal Jurnal Armada Informatika Pascal: Journal of Computer Science and Informatics JITSI : Jurnal Informatika Dan Teknologi Sistem Informasi
Desman Karya Jaya Zega
Universitas Budi Darma
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Library & Information Science Social Sciences

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Driver Scheduling Priority Decision Support System Using the Weighted Product Method Fati Harmonis Zai; Desman Karya Jaya Zega; Fadlina; Fince Tinus Waruwu
Jurnal Armada Informatika Vol 10 No 1 (2026): Juni
Publisher : STMIK Methodist Binjai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36520/jai.v10i1.277

Abstract

Driver scheduling is an important process in public transportation operations because it is related to workload distribution, fleet readiness, and service quality. At Koperasi Pengangkutan Umum Medan (KPUM), driver scheduling priorities are still determined conventionally, making decisions potentially subjective and difficult to trace quantitatively. This study aims to develop a web-based Decision Support System (DSS) to determine driver scheduling priorities using the Weighted Product method. The assessment criteria consist of route mastery, discipline, work experience, working time, and health condition. Data were obtained through observation, interviews, and internal assessment of ten active drivers. The analysis process includes weight normalization, construction of compatibility ratings, S vector calculation, V vector calculation, and alternative ranking. The calculation results show that alternative A8, Binarto, obtains the highest preference value of 0.1311, followed by A5 with 0.1265 and A9 with 0.1234. Functional testing indicates that the login feature, data management, calculation process, and ranking display run according to the test scenarios. The proposed system can assist KPUM in preparing scheduling priorities in a faster, measurable, and more transparent manner.
A Comparative Analysis of the Efficiency of Blockchain Consensus Algorithms: Proof of Work, Proof of Stake, Delegated Proof of Stake, and Practical Byzantine Fault Tolerance Desman Karya Jaya Zega; Laurensius So Putra Jaya Halawa; Siaman Lase; Melissa Putri Hutabarat
Pascal: Journal of Computer Science and Informatics Vol. 3 No. 02 (2026): Pascal: Journal of Computer Science and Informatics
Publisher : Devitara Innovations

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Abstract

The rapid development of blockchain technology has accelerated the adoption of distributed systems across various sectors, including financial services, supply chain management, the Internet of Things (IoT), and digital government. Consensus algorithms play a fundamental role in blockchain by ensuring transaction validity and data consistency without relying on a centralized authority. Since each consensus mechanism exhibits different characteristics, a comparative analysis is required to identify its strengths and limitations. This study aims to analyze and compare the efficiency of four major blockchain consensus algorithms, namely Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), and Practical Byzantine Fault Tolerance (PBFT). The research employed a comparative literature study by collecting, selecting, and analyzing relevant scientific publications. The comparison was conducted based on transaction throughput, energy efficiency, communication complexity, transaction finality, network characteristics, and fault tolerance. The results indicate that no single consensus algorithm provides optimal performance across all evaluation parameters. PoW offers high security and decentralization but suffers from high energy consumption and low throughput. PoS provides a balanced trade-off among energy efficiency, security, and scalability, while DPoS improves transaction capacity through a delegated validator mechanism. PBFT achieves high throughput and instant transaction finality but is more suitable for permissioned blockchain environments with a limited number of participating nodes. This study proposes a comparative analysis framework that can serve as a reference for selecting an appropriate consensus algorithm based on blockchain implementation requirements.
Penerapan Metode Naïve Bayes Untuk Mengklasifikasi Bunga Iris Desman Karya Jaya Zega; Desman Karya Jaya Zega; Muhammad rizki arisandi berutu; David Soteriel Agung Ndruru; Putri Shabna Dewi Sinaga; Erlangga Oktaviano; Anisa salsabila
Interaksi : Jurnal Informatika Dan Teknologi Sistem Informasi Vol 1 No 2 (2026): Mei
Publisher : PT. Ndruru Jaya Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67763/jitsi.v1i2.67

Abstract

Penelitian ini memaparkan evaluasi komparatif mengenai algoritma Naive Bayes dan Decision Tree dalam konteks penerapan pada beragam domain data. Domain-domain yang dieksplorasi mencakup pengenalan spesies Iris, penentuan komposisi material daging, kategorisasi subjek, serta penilaian tingkat kemiringan di sektor pariwisata yang membutuhkan analisis yang cermat. Tujuan utama dari studi ini adalah untuk menilai efektivitas, keunggulan khas, serta batasan yang dimiliki oleh kedua model prediktif tersebut ketika dihadapkan pada karakteristik data yang berbeda. Metodologi penelitian melibatkan implementasi sistematis dari kedua algoritma pada serangkaian dataset spesifik dengan prosedur eksperimen yang terstruktur. Pengukuran kinerja didasarkan pada metrik-metrik standar, yaitu akurasi, presisi, recall, dan F1-score. Hasil analisis menunjukkan bahwa kedua algoritma menampilkan tingkat kinerja yang sebanding, namun dengan titik kekuatan yang berbeda-beda yang bergantung erat pada sifat data yang dianalisis. Decision Tree terbukti menawarkan interpretabilitas yang superior dan memiliki kapabilitas lebih baik dalam memodelkan hubungan data non-linear. Sebaliknya, Naive Bayes menunjukkan efisiensi yang optimal, terutama pada kasus di mana fitur-fitur memiliki tingkat independensi tinggi dan ketika diolah menggunakan jumlah data pelatihan yang besar. Temuan-temuan ini memberikan kontribusi penting dalam mengidentifikasi skenario aplikasi yang paling sesuai untuk masing-masing algoritma dalam menyelesaikan berbagai permasalahan data mining secara efektif. Hasil pengujian menunjukkan bahwa metode Naive Bayes mampu mencapai akurasi sebesar 93,3%, dengan nilai precision, recall, dan F1-score yang tinggi pada setiap kelas. Hasil ini menunjukkan bahwa metode Naive Bayes memiliki kinerja yang baik dan efektif dalam melakukan klasifikasi bunga Iris..
Co-Authors Anisa salsabila David Soteriel Agung Ndruru Desman Karya Jaya Zega Erlangga Oktaviano Fadlina Fati Harmonis Zai Fince Tinus Waruwu Laurensius So Putra Jaya Halawa Melissa Putri Hutabarat Muhammad rizki arisandi berutu Putri Shabna Dewi Sinaga Siaman Lase