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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Intelek Dan Cendikiawan Nusantara
Zulfa Ar Rahman
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Computer Science & IT Economics, Econometrics & Finance Education Social Sciences Other

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IMPLEMENTASI METODE FUZZY SUGENO PADA PENENTUAN HARGA EMAS 24 KARAT PADA KOTA MEDAN Khairul Saleh; Zulfa Ar Rahman; Emi Dea
Jurnal Intelek Dan Cendikiawan Nusantara Vol. 2 No. 6 (2025): Desember 2025 - Januari 2026
Publisher : PT. Intelek Cendikiawan Nusantara

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Abstract

Harga emas 24 karat di Kota Medan cenderung mengalami perubahan yang dipengaruhi oleh beberapa faktor, seperti harga emas dunia, nilai tukar rupiah, dan kondisi permintaan pasar. Fluktuasi harga tersebut menyulitkan masyarakat dan pelaku usaha dalam menentukan harga emas yang tepat dan kompetitif. Penelitian ini bertujuan untuk mengimplementasikan metode Fuzzy Sugeno dalam menentukan harga emas 24 karat di Kota Medan. Metode Fuzzy Sugeno digunakan karena mampu menangani ketidakpastian data dan menghasilkan keluaran berupa nilai tegas (crisp). Variabel input yang digunakan meliputi harga emas dunia, nilai tukar rupiah, dan tingkat permintaan emas, sedangkan variabel output adalah harga emas 24 karat. Tahapan penelitian meliputi proses fuzzifikasi, pembentukan aturan fuzzy, inferensi Sugeno, dan defuzzifikasi menggunakan metode rata-rata berbobot. Hasil penelitian menunjukkan bahwa metode Fuzzy Sugeno mampu memberikan estimasi harga emas yang mendekati harga pasar, sehingga dapat dijadikan sebagai sistem pendukung keputusan dalam penentuan harga emas 24 karat di Kota Medan
PENERAPAN JARINGAN SYARAF TIRUAN UNTUK PREDIKSI JUMLAH PENUMPANG KERETA API MENGGUNAKAN ALGORITMA BACKPROPAGATION Zulfa AR Rahman; Wanayumini
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 3 (2026): June 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i3.6645

Abstract

The number of rail passengers on the Kisaran–Medan route varies over time due to a number of variables, including local economic situations, national holidays, peak travel seasons, and current transportation regulations. The administration of Kisaran Station finds it challenging to manage train fleets, organize travel timetables, and enhance service quality due to this unpredictability. Using historical data from 2019 to 2025, this study intends to apply an Artificial Neural Network (ANN) with the Backpropagation technique to forecast the number of train passengers at Kisaran Station. With a learning rate of 0.25 and a sigmoid binary activation function, the system was constructed using a 7-5-1 network architecture with seven input neurons (yearly data from 2019–2025), five hidden layer neurons, and one output neuron. The data was split into 70% training data (January–July) and 30% testing data (August–December) after being adjusted to the interval 0.1–0.9 using the Min-Max technique. At epoch 822, the training process reached a convergence. For most months, including March (predicted: 647,271; actual: 642,870 passengers) and April (predicted: 999,728; actual: 996,320 passengers), the model was able to produce predictions that were close to actual values, according to testing results. However, there were differences in some months with seasonal spikes. It is anticipated that PT Kereta Api Indonesia and the regional government would use the created prediction system as a decision-support tool to plan transportation services and enhance the general quality of railway services.
Co-Authors Emi Dea Khairul Saleh Wanayumini