Abdulloh Hamid
Department of Mathematics, Faculty of Science and Technology, UIN Sunan Ampel Surabaya, Indonesia

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A STUDY ON THE APPLICABILITY OF TRAPEZOIDAL FUZZY AHP WITH FEATURE SELECTION: THE CASE OF SKSS SCHOLARSHIP RECIPIENTS AT BAZNAS EAST JAVA Syamil Waris Dien Muhammad; Abdulloh Hamid; Hani Khaulasari; Dian Candra Rini Novitasari; Moh Hafiyusholeh
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2027-2044

Abstract

The One Family One Graduate (SKSS) scholarship program, managed by BAZNAS East Java, aims to alleviate the financial burden of higher education for underprivileged communities. However, the absence of clearly defined weights for each selection criterion may lead to unfairness in the selection process. This study aims to determine the objective weights of each criterion and to rank prospective scholarship recipients using the Trapezoidal Fuzzy AHP approach. The data were obtained from 78 scholarship applicants for the 2024 SKSS period and from questionnaires completed by three expert respondents (expert judgment). Feature selection was conducted to identify the most relevant criteria, resulting in 13 selected variables are tuition fee per semester (K₁), father's latest education level (K₂), father's income (K₃), mother's latest education level (K₄), mother's income (K₅), house size (K₆), amount of family installments (K₇), number of parental dependents (K₈), income of working family members (K₉), type of transportation used to campus (K₁₀), distance from home to campus (K₁₁), monthly allowance (K₁₂), and monthly income if the student is working (K₁₃). The results show that the criterion with the highest weight is tuition fee per semester (0.139142), while the lowest is Type of transportation to campus (0.059970). The highest priority subject is Subject 74 (S_74) with a total weight of 0.7964, whereas Subject 23 (S_23) ranks lowest with a total weight of 0.7723. These findings are expected to enhance the objectivity and fairness of the SKSS scholarship selection process.
OPTIMIZATION OF ARIMA RESIDUALS USING LSTM IN STOCK PRICE PREDICTION OF PT MEDCO ENERGI INTERNASIONAL TBK Achmad Fachril Yusuf Ababil; Abdulloh Hamid; Hani Khaulasari; Dian Candra Rini Novitasari; Wika Dianita Utami
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1405-1420

Abstract

The capital market plays an important role in the economy by providing a means for companies to obtain capital and as a place to invest. Stocks are one of the popular investment instruments because their potential profits are attractive to investors. The stocks used in this study are PT Medco Energi Internasional Tbk (MEDC) shares. The purpose of this study is to obtain the optimal ARIMA-LSTM residual optimization model, how much the accuracy, and to predict Medco stock prices for the next 8-month period. The data used starts from January 4, 2021, to October 31, 2024, was obtained from the yahoofinance.com website. The ARIMA model, which is known to be effective in handling linear data, will be combined with LSTM. The use of residuals in the LSTM model can help LSTM capture patterns in the entire stock data so as to increase prediction accuracy. The research results obtained are the optimal ARIMA-LSTM optimization model, namely, ARIMA ([5,9],1,[5,9,11]) and LSTM with the best hyperparameter, namely, hidden layer 64, batch size 16, and learning rate 0.01. The accuracy of the ARIMA-LSTM optimization model is classified as very accurate, with a MAPE value of 0.3%. Medco Energi’s stock price for the next 8-month period is predicted to increase from IDR1312 to IDR1430 or an increase of 9%.