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All Journal PROSIDING SENATEK FAKULTAS TEKNIK UMP Jurnal Teknik Informatika (JUTIF) The Health Researchers Journal
Aslan Alwi, Aslan
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Computer Science & IT Engineering Public Health

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MEMBANGUN APLIKASI CONTENT MANAGEMENT SYSTEM (CMS) UNTUK SEMBARANG SISTEM PENDUKUNG KEPUTUSAN BERBASIS PERMODELAN MULTIPLE ATTRIBUTE DECISION MAKING (MADM) Alwi, Aslan
PROSIDING SENATEK FAKULTAS TEKNIK UMP 2015: PROSIDING SENATEK TAHUN 2015, 28 November 2015
Publisher : PROSIDING SENATEK FAKULTAS TEKNIK UMP

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Abstract

Penelitian ini berusaha untuk mengemukakan sebuah pendapat bahwa sebuahcontent management system yang ditujukan untuk sebuah sistem pendukungkeputusan adalah dapat dibuat. Pendapat ini melihat bahwa sebuah sistem SPKdapat dibuat bebas konten artinya bahwa sistem SPK tidak bergantung kepadasituasi permasalahan pengambilan keputusan. Sistem SPK seharusnya dapatdigunakan untuk sebarang situasi permasalahan pengambilan keputusan, apakahitu pengambilan keputusan di dalam perdagangan, dalam administrasi sekolah,dalam manajemen industri, manajemen rumah sakit dan sebagainya. sehinggamenggambarkan sebuah sistem SPK yang bersifat independen terhadap konten.Dalam artian bahwa data apapun yang dimasukkan, dengan tujuan untukmelakukan pengambilan keputusan dari berbagai situasi pengambilan keputusanmaka itu dapat dilakukan oleh sistem. Dalam penelitian ini, pengertian sebarangsituasi pengambilan keputusan dibatasi pada assumsi bahwa situasi pengambilankeputusan dapat dirumuskan sebagai proses pemilihan dari beberapa alternatifyang jumlahnya berhingga berdasarkan sejumlah kriteria yang juga jumlahnyaberhingga. Sehingga situasi itu secara global dapat dinyatakan dalam permodelanmultiple attribute decision making atau MADM.Kata kunci : Multiple attribute decision making, content management system,sistem pendukung keputusan, matriks MADM, bebas konten.
Self-Harm Measurements Mashudi, Sugeng; Isroin, Laily; Alwi, Aslan
The Health Researcher's Journal Vol. 1 No. 01 (2024): The Health Researchers Journal
Publisher : The SDGS Forum Communication

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Background: Self-harm could be a basic nursing mental health issue for adolescents/young grown-ups. Aims: The purpose of this study is to explain the self-harm measurement tool. Method: Four databases were systematically searched (Google Scholar, PubMed, Scopus and Web of Science). Results and Discussion: Eight frequently used self-harm assessment tools were identified and assessed for risk of bias, criteria for good measurement properties, and quality of evidence using the COSMIN checklist. Of these, two tools had sufficient evidence of internal consistency (ISAS, QNSSI), and one had been frequently used with adults (NSSI-AT). Conclusion: These five tools may have the potential for use in adults for content validity and measurement properties in the general population.
K-ALLY BASED DYNAMIC FUZZY CLUSTERING FOR GEOPOLITICAL ALLIANCE ANALYSIS: A CASE STUDY INSPIRED BY THE RUSSIAN-UKRAINIAN CONFLICT Munirah; Alwi, Aslan; Sudarno; Triyanto, Andy
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.907

Abstract

Geopolitical alliances are often based on a combination of factors such as geographic proximity, military strength, and strategic interests. In this research, we introduce the K-Ally algorithm based on Dynamic Fuzzy Clustering to dynamically analyze alliance patterns between countries. Using fuzzy logic and adaptive thresholds, this algorithm evaluates the potential benefits of alliances based on key attributes, such as geographic distance and power differences. This study is inspired by the allied dynamics that emerged in the Russian-Ukrainian war, where changes in strategy and international relations were key to the continuation of the conflict. The paper also compare this algorithm with the K-Means method commonly used in geopolitical data analysis. Experimental results show that K-Ally based on Dynamic Fuzzy Clustering is able to capture alliance dynamics better than K-Means, especially in conditions of uncertainty or attribute imbalance between countries. This research contributes to the development of new analytical tools for the study of geopolitics and international conflict.
Co-Authors Laily Isroin, Laily Mashudi, Sugeng Munirah Sudarno Triyanto, Andy