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Pengukuran Kualitas layanan Internet di Universitas Sembilanbelas November Kolaka Berdasarkan QoS dan QoE Paliling, Alders; Mardianto, Mardianto; Sutoyo, Muhammad Nurtanzis
e-Jurnal JUSITI (Jurnal Sistem Informasi dan Teknologi Informasi) Vol. 12 No. 2 (2023): e-Jurnal JUSITI
Publisher : Universitas Dipa Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36774/jusiti.v12i2.1425

saat ini, kepuasan pengguna menjadi salah satu topik terpenting yang diperhatikan oleh penyedia layanan. Quality of Service (QoS) adalah kemampuan suatu jaringan untuk menyediakan jaminan dan performa jaringan seperti delay, throughput, dan packet loss sedangkan Quality of Experience (QoE) merupakan pendekatan terhadap kualitas layanan internet, yang dapat menjelaskan pentingnya sebuah perubahan layanan berdasarkan apa yang dirasakan pengguna saat menikmati layanan yang diberikan di USN Kolaka memiliki akses layanan internet yang ketersediaan akses internet seperti Video call/Conference, VideoStreaming, dan Web Browsing. Tujuan dari penelitian ini adalah mengguji apakah pengukuran kualitas internet secara teknis berbanding lurus dengan pengukuran pengalaman pengguna.Berdasarkan hasil uji pengukuran Quality of Service (QoS) dan Quality Of Experience (QoE) bahwa perbandingan kualitas dari faktor teknis (QoS) yang diukur memiliki nilai yang bagus dari parameter uji seperti delay, throughput, packet loss dan jitter sedangkan parameter dari kaulitas non teknis (QoE) seperti content quality, system quality dan service quality diperoleh nilai QoE yang di ukur berdasarkan nilai MOS memiliki kualitas baik sehingga dapat disimpulkan bahwa berdasarkan nilai QoS dan QoE layanan internet di Universitas Sembilanbelas November Kolaka dikategorikan baik.

Optimasi Pengelolaan Data Mahasiswa dalam Sistem Pendukung Keputusan Pemilihan Asisten Laboratorium Sutoyo, Muh. Nurtanzis; Akbar, Muh. Hajar; Paliling, Alders; Mardianto, Mardianto
Journal Research on Computing Knowledge Vol. 1 No. 1 (2024): November 2024
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar

The selection of laboratory assistants is a crucial process that requires decision-making based on various criteria, such as academic performance, activeness, technical skills, and communication abilities. However, manual processes often face challenges such as subjectivity and inefficiency. This study aims to develop a Decision Support System (DSS) based on the Simple Additive Weighting (SAW) method to optimize the selection of laboratory assistants. We utilize the SAW method because it can integrate criterion weights and normalize data to produce objective preference values. We conducted a simulation using dummy data from 10 students and four criteria, applying weights determined through discussions with laboratory managers. The results show that student A5 achieved the highest preference score (1.000), reflecting optimal performance across all criteria. The developed system also demonstrated its ability to enhance the transparency, efficiency, and accuracy of the selection process. The implementation of this system offers a practical solution for managing student data and making fairer decisions, with potential applications in other selection contexts, such as scholarships or academic awards

Optimization of PKK Anawoi Village in Increasing Digital Literacy and Coastal Tourism-Based Creative Industries: Optimalisasi PKK Kelurahan Anawoi dalam Meningkatkan Literasi Digital dan Industri Kreatif Berbasis Pariwisata Pesisir Sarmadan, Sarmadan; Sakti, La Ode Awal; Sutoyo, Muhammad Nurtanzis; Baharuddin, Zubair; Saputra, Nanda
Dinamisia : Jurnal Pengabdian Kepada Masyarakat Vol. 9 No. 1 (2025): Dinamisia: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/dinamisia.v9i1.24990

Kampoh Malasso – Kampung Bajo Anaiwoi is a floating village located in Anaiwoi Village, Tanggetada District, Kolaka Regency, Southeast Sulawesi Province. The majority of people in this floating village live by utilizing marine products, but limitations in business management and product marketing are the main obstacles in improving their standard of living. Seeing this condition, the USN Kolaka Community Partnership Program (PKM) Team tried to collaborate with the Anaiwoi Village PKK Team to help the local community through training and mentoring that focuses on aspects of management, product packaging, and digital marketing in order to increase literacy, development of MSMEs and creative industries based on coastal tourism. The PKM implementation method is divided into 5 stages, namely: socialization, training, application of technology, mentoring and evaluation, and program sustainability. This service resulted in several important achievements related to the level of empowerment of PKK partners in Anaiwoi Village, namely: 1) Increasing Managerial Knowledge: Training participants succeeded in improving their managerial skills, especially in preparing plans and managing PKK programs; 2) Improving Packaging Skills, where partners are skilled in packaging marine products and MSMEs, such as packaging for Malasso Dried Fish, Anaiwoi Green Banana, SaPa Ongol-Ongol (Coconut Sago), Fresh Smoothies, Malasso Village Krips (Cassava Chips), as well as digital printing creative industries (shirt screen printing, etc.); and 3) Improving Digital Marketing Skills: Partners are able to utilize digital technology to market their products more widely through websites and social media, as well as improve their skills in creating promotional content that has an impact on strengthening literacy at the same time.

SISTEM BANTU PENENTUAN UKT MAHASISWA DENGAN METODE WEIGHTED PRODUCT Sutoyo, Muh. Nurtanzis; Pradipta, Anjar; Paliling, Alders; Miftachurohmah, Nisa
Indonesian Journal of Business Intelligence (IJUBI) Vol 6 No 2 (2023): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v6i2.3679

Penelitian ini berfokus pada pengembangan sistem bantu perhitungan untuk menentukan Uang Kuliah Tunggal (UKT) di institusi pendidikan tinggi menggunakan metode Weighted Product. Tujuan dari sistem ini adalah untuk menciptakan proses penentuan UKT yang lebih objektif, transparan, dan efisien. Metode Weighted Product digunakan karena kemampuannya dalam menangani multi-kriteria yang melibatkan berbagai variabel seperti pendapatan orang tua, kondisi orang tua, pendidikan, pekerjaan dan ada tidaknya bantuan dari pemerintah. Penelitian ini melibatkan tahap-tahap seperti pengumpulan data, pembobotan kriteria, dan perhitungan skor akhir. Hasil penelitian menunjukkan bahwa sistem ini mampu menghasilkan keputusan yang konsisten dan dapat diandalkan, dengan tingkat akurasi yang signifikan dalam menentukan kelompok UKT untuk setiap individu. Sistem ini diharapkan dapat menjadi solusi dalam menyederhanakan proses penentuan UKT serta meningkatkan keadilan dan keakuratan dalam penentuan biaya pendidikan

Optimalisasi Prediksi Lama Studi Mahasiswa Menggunakan Rough Set dan Case-Based Reasoning Sutoyo, Muhammad Nurtanzis; Sutoyo, Muh. Nurtanzis; Adawiyah, Rabiah
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 1: Februari 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026131

Prediksi lama studi mahasiswa menjadi aspek penting dalam perencanaan akademik, evaluasi kinerja, serta identifikasi potensi keterlambatan kelulusan. Penelitian ini mengusulkan pendekatan hibrida dengan mengintegrasikan metode Rough Set dan Case-Based Reasoning untuk meningkatkan akurasi prediksi kelas lama studi mahasiswa. Metode Rough Set digunakan untuk mengekstraksi aturan klasifikasi berbasis kombinasi atribut IPK, status bekerja, dan status beasiswa, serta menghitung probabilitas kelas pada boundary region. Di sisi lain, metode CBR dimanfaatkan untuk menghitung similarity antar kasus berdasarkan kemiripan atribut, termasuk jumlah SKS yang dinormalisasi. Hasil prediksi dilakukan melalui integrasi probabilitas dari Rough Set dan similarity CBR menggunakan bobot kombinasi sebesar 0.6 dan 0.4. Pada pengujian kasus baru, diperoleh lima kasus historis paling mirip dengan similarity 0.97, empat di antaranya tergolong “Sangat Terlambat” dan satu “Terlambat”. Sementara itu, probabilitas dari Rough Set menunjukkan distribusi 0.667 untuk “Sangat Terlambat” dan 0.333 untuk “Terlambat”. Hasil integrasi memberikan skor akhir sebesar 0.720 untuk “Sangat Terlambat” dan 0.280 untuk “Terlambat”, yang menunjukkan sistem prediksi cenderung kuat terhadap kategori “Sangat Terlambat”. Pendekatan gabungan ini terbukti efektif dalam menggabungkan kekuatan generalisasi dari Rough Set dan fleksibilitas adaptif dari CBR, sehingga dapat digunakan sebagai sistem pendukung keputusan dalam evaluasi akademik berbasis data historis. Abstract The forecast of student study length is essential for academic planning, performance assessment, and recognizing possible graduation delays. This study presents a hybrid methodology that combines Rough Set theory and Case-Based Reasoning techniques to enhance the precision of predicting student study length classifications. The Rough Set approach is employed to derive classification rules from combinations of attributes, including GPA, job status, and scholarship status, as well as to compute class probabilities inside the boundary region. Simultaneously, the CBR approach is utilized to assess similarity between cases based on attribute similarity, including normalized credit hours (SKS). The prediction results are produced by integrating Rough Set probability and CBR similarity, utilizing weighted values of 0.6 and 0.4, respectively. In the test case, five historical cases with similarity scores of 0.97 were identified, four classified as “Very Late” and one as “Late”. Rough Set probability were 0.667 for “Very Late” and 0.333 for “Late”. The conclusive integrated scores were 0.720 for “Very Late” and 0.280 for “Late”, signifying that the algorithm predominantly forecasts the “Very Late” category. This hybrid methodology adeptly integrates the generalization capabilities of Rough Set theory with the adaptive versatility of Case-Based Reasoning, rendering it appropriate as a decision support system for academic assessment grounded in historical data.

Optimalisasi Prediksi Lama Studi Mahasiswa Menggunakan Rough Set dan Case-Based Reasoning Sutoyo, Muhammad Nurtanzis; Sutoyo, Muh. Nurtanzis; Adawiyah, Rabiah
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 1: Februari 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026131

Prediksi lama studi mahasiswa menjadi aspek penting dalam perencanaan akademik, evaluasi kinerja, serta identifikasi potensi keterlambatan kelulusan. Penelitian ini mengusulkan pendekatan hibrida dengan mengintegrasikan metode Rough Set dan Case-Based Reasoning untuk meningkatkan akurasi prediksi kelas lama studi mahasiswa. Metode Rough Set digunakan untuk mengekstraksi aturan klasifikasi berbasis kombinasi atribut IPK, status bekerja, dan status beasiswa, serta menghitung probabilitas kelas pada boundary region. Di sisi lain, metode CBR dimanfaatkan untuk menghitung similarity antar kasus berdasarkan kemiripan atribut, termasuk jumlah SKS yang dinormalisasi. Hasil prediksi dilakukan melalui integrasi probabilitas dari Rough Set dan similarity CBR menggunakan bobot kombinasi sebesar 0.6 dan 0.4. Pada pengujian kasus baru, diperoleh lima kasus historis paling mirip dengan similarity 0.97, empat di antaranya tergolong “Sangat Terlambat” dan satu “Terlambat”. Sementara itu, probabilitas dari Rough Set menunjukkan distribusi 0.667 untuk “Sangat Terlambat” dan 0.333 untuk “Terlambat”. Hasil integrasi memberikan skor akhir sebesar 0.720 untuk “Sangat Terlambat” dan 0.280 untuk “Terlambat”, yang menunjukkan sistem prediksi cenderung kuat terhadap kategori “Sangat Terlambat”. Pendekatan gabungan ini terbukti efektif dalam menggabungkan kekuatan generalisasi dari Rough Set dan fleksibilitas adaptif dari CBR, sehingga dapat digunakan sebagai sistem pendukung keputusan dalam evaluasi akademik berbasis data historis. Abstract The forecast of student study length is essential for academic planning, performance assessment, and recognizing possible graduation delays. This study presents a hybrid methodology that combines Rough Set theory and Case-Based Reasoning techniques to enhance the precision of predicting student study length classifications. The Rough Set approach is employed to derive classification rules from combinations of attributes, including GPA, job status, and scholarship status, as well as to compute class probabilities inside the boundary region. Simultaneously, the CBR approach is utilized to assess similarity between cases based on attribute similarity, including normalized credit hours (SKS). The prediction results are produced by integrating Rough Set probability and CBR similarity, utilizing weighted values of 0.6 and 0.4, respectively. In the test case, five historical cases with similarity scores of 0.97 were identified, four classified as “Very Late” and one as “Late”. Rough Set probability were 0.667 for “Very Late” and 0.333 for “Late”. The conclusive integrated scores were 0.720 for “Very Late” and 0.280 for “Late”, signifying that the algorithm predominantly forecasts the “Very Late” category. This hybrid methodology adeptly integrates the generalization capabilities of Rough Set theory with the adaptive versatility of Case-Based Reasoning, rendering it appropriate as a decision support system for academic assessment grounded in historical data.

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