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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.

EFEKTIVITAS KOMBINASI VIGENÈRE CIPHER DAN HILL CIPHER DALAM PENGAMANAN INFORMASI Sutoyo, Muh. Nurtanzis; Qammaddin, Qammaddin; Rahayu, Rahayu; Kariani, Ni Komang Ria
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.6792

Keamanan informasi merupakan aspek krusial dalam era digital yang terus berkembang, terutama di tengah meningkatnya ancaman kriptanalisis terhadap algoritma enkripsi konvensional. Penelitian ini bertujuan untuk mengevaluasi efektivitas kombinasi algoritma kriptografi Vigenère Cipher dan Hill Cipher sebagai pendekatan berlapis dalam melindungi data dari serangan analisis frekuensi. Vigenère Cipher menawarkan substitusi multi-abjad yang berubah-ubah berdasarkan kunci, sementara Hill Cipher memanfaatkan operasi matriks untuk mengenkripsi blok teks secara bersamaan. Hasil penelitian menunjukkan bahwa kombinasi kedua algoritma ini dapat meningkatkan kompleksitas ciphertext, sehingga mengurangi kerentanan terhadap serangan berbasis analisis frekuensi. Namun, tantangan utama yang diidentifikasi adalah pada perhitungan invers matriks dalam Hill Cipher, yang membutuhkan ketelitian tinggi, serta keefektifan kombinasi ini lebih optimal pada data dengan panjang genap atau yang dapat dipadati sesuai persyaratan blok Hill Cipher. Dengan demikian, penelitian ini berkontribusi pada peningkatan keamanan enkripsi melalui pendekatan berlapis, meskipun perlu disesuaikan dengan keterbatasan sumber daya

The selection of SNMPTN applicants using the TOPSIS and rank order centroid (ROC) methods Sutoyo, Muhammad Nurtanzis; Mangkona, Andi Tenri Sumpala
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.936.272-284

One of the main functions of higher education is to develop personality abilities and shape the character of a dignified nation's civilization. One of the entry pathways for new student admissions at State Universities is the SNMPTN Path. While the number of participants who are accepted by looking at the quota on the SNMPTN line is only 20 percent of the capacity. Decision Support System as a solution that can assist policymakers in determining a decision. In this study, a decision support system was built using the TOPSIS method for selecting and ROC as a weighting for each criterion. Based on the results of the selection test for SNMPTN participants using the TOPSIS and ROC methods as weighting, it proves that it can display the cumulative ranking results of each alternative based on the criteria values owned.

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