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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Jurnal Ekonomi dan Pendidikan JPMS (Jurnal Pendidikan Matematika dan Sains) Jurnal Penelitian Saintek Pythagoras: Jurnal Matematika dan Pendidikan Matematika EKSAKTA: Journal of Sciences and Data Analysis Jurnal Ilmu Dasar Jurnal Sains Dasar TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Infinity Jurnal Riset Pendidikan Matematika Jurnal Pengukuran Psikologi dan Pendidikan Indonesia (JP3I) BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Beta: Jurnal Tadris Matematika JUMLAHKU: Jurnal Matematika Ilmiah STKIP Muhammadiyah Kuningan MIND (Multimedia Artificial Intelligent Networking Database) Journal JP3M (Jurnal Penelitian Pendidikan dan Pengajaran Matematika) Jurnal Pendidikan Indonesia (Japendi) DEDIKASI : Jurnal Pengabdian Kepada Masyarakat Jurnal Statistika dan Sains Data Jurnal Kajian dan Terapan Matematika International Journal of Mathematics and Science Education Jurnal Infinity Jurnal Matematika Ilmiah Universitas Muhammadiyah Kuningan Mathematics Education Journal
Agus Maman Abadi
Universitas Negeri Yogyakarta
Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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The Influence of the STEM Approach on High School Students' Mathematical Problem Solving Ability in Trigonometry Zulfa Lailaturrosidah; Agus Maman Abadi
International Journal of Mathematics and Science Education Vol. 2 No. 4 (2025): November : International Journal of Mathematics and Science Education
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijmse.v2i4.244

Abstract

This study aims to analyze the effectiveness of the STEM approach and the scientific approach in learning Trigonometry in terms of students’ mathematical problem-solving ability and self-efficacy. The research employed a quasi-experimental method with a nonequivalent comparison group design. The study population consisted of all Grade X MIPA students at MA Al-Ma’had An-Nur in the 2023/2024 academic year, with the sample taken from classes X MIPA 1 and X MIPA 2. Data collection instruments included pre-test and post-test assessments of mathematical problem-solving ability and a student self-efficacy questionnaire. Effectiveness criteria were based on post-test averages exceeding 75 for problem-solving ability and self-efficacy scores above 72, as well as improvements from pre-test results. Data were analyzed using the t-test at a 5% significance level to determine learning effectiveness in both groups. Differences in students’ initial conditions and treatment effects were examined using Hotelling’s T² and the N-Gain score test. An independent-sample t-test of N-Gain was used to compare the superiority between the two approaches. The results indicated that both the STEM and scientific approaches were effective in improving students’ mathematical problem-solving ability and self-efficacy. The STEM approach significantly influenced both variables and was superior in enhancing problem-solving ability, although not superior in improving students’ self-efficacy.
Prediksi Rate of Penetration pada Pengeboran Minyak Bumi dengan Elman Recurrent Neural Network AIZIYAH, ELSA; HARYANTO, ALLEN; ABADI, AGUS MAMAN
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 10, No 2 (2025): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v10i2.145-161

Abstract

ABSTRAKPenelitian ini bertujuan memprediksi laju penetrasi (ROP) guna mempercepat waktu pengeboran dan menekan biaya operasional. Metode yang digunakan adalah Elman Recurrent Neural Network (ERNN) dengan algoritma backpropagation, yang dipilih karena kemampuannya dalam mengenali pola data sekuensial pada data pengeboran. Data yang digunakan 2613 data ASCII Mudlogging minyak bumi dari PT Geotama Jogja dengan 5 variabel input, yaitu Kedalaman Vertikal Sejati atau TVD (m), Beban Mata Bor atau WOB (klbs), Kepadatan Sirkulasi Ekuivalen atau ECD (SG), Mud Weight in atau MWI (SG), dan Total Kecepatan Rotasi Pahat atau TRPM. Sedangkan variabel outputnya yaitu laju penetrasi atau ROP (m/hr). Data dihaluskan menggunakan Savitzky-Golay filter dan dibagi data training dan data testing yang sebesar 90% dan 10%. Model ERNN terbaik yang diperoleh yaitu 5 variabel input, 17 neuron tersembunyi, dan 1 variabel output. Nilai MAPE data training sebesar 16.18%, dengan akurasi 83.82%. Sedangkan nilai MAPE data testing sebesar 15.48%, sehingga akurasinya 84.52%.  Kata kunci: Elman Recurrent Neural Network, laju penetrasi, prediksi, minyak bumi, MAPE ABSTRACTThis study aims to predict the rate of penetration (ROP) to speed up drilling time and reduce operational costs. The method used is the Elman Recurrent Neural Network (ERNN) with the backpropagation algorithm, which was chosen because of its ability to recognize sequential data patterns in drilling data. The data used are 2613 ASCII Mudlogging data from PT Geotama Jogja with 5 input variables, namely True Vertical Depth or TVD (m), Drill Bit Load or WOB (klbs), Equivalent Circulation Density or ECD (SG), Mud Weight in or MWI (SG), and Total Tool Rotation Speed or TRPM. While the output variable is the rate of penetration or ROP (m/hr). The data is smoothed using the Savitzky-Golay filter and divided into training data and testing data of 90% and 10%. The best ERNN model obtained is 5 input variables, 17 hidden neurons, and 1 output variable. The MAPE value of the training data is 16.18%, so the accuracy is 83.82%. Meanwhile, the MAPE value for the testing data was 15.48%, resulting in an accuracy of 84.52%.  Keywords: Elman Recurrent Neural Network, penetration rate, prediction, petroleum, MAPE
Co-Authors Abd. Haris Aditya Wisnugraha Sugiyarto Agustina, Ni Luh Ika Tri Ahmad Ahmad Ahmad Ahmad Ahmad Nizar Rangkuti Ahmad Syajili Aisah Badaini AIZIYAH, ELSA Ali Muhson Ali, Rade Muhammad Ali, Raden Muhammad Anggun Badu Kusuma Anis Ulfah Mustaqim Anis Ulfah Mustaqim Annisa Fitri Aprisal Aprisal Aprisal, Aprisal Arifudin Prabowo Kurniawan Astri Wahyuni Asyik Andi Arnomo Bertolomeus Laksana Jayadri Dhoriva Urwatul Wutsqa Dita Aldila Krisma Djidu, Hasan Emut . Emut Emut Emut Emut Ezra Putranda Setiawan Fadhila, Sinta Oktavia Nur Farida Hanum Farida Hanum Hari Purnomo Susanto Harini, Lusi HARYANTO HARYANTO, ALLEN Heri Retnawati Hidayah Nurul Hasanah Zen Indah Puspita Joko Sungkono Kartika, Yuli Karyati Karyati Kusuma, Anggun Badu lukmana, allen haryanto Ma'rif, Erni Fatun Mahardika, Bintang Wira Mansyaroh, Akhid Khirohmah Mohammad Saeful Amri Muhammad Najib Mubarrok Musthofa . Musthofa Musthofa Musthofa Musthofa Nirmasari, Yumanita Nisrina Amaliyah Wibowo Nurlia Ningsih Oktarina, Anisa Dwi Pratama, Agustian Arditya Rani Mita Sari Rini Dwi Astuti Rizka Nur Pratiwi Rizky Oktora Prihadini Eka Putri Samsubar Saleh Samuel Hasibuan Sri Andayani Subanar . Sugiyarto, Aditya Wisnugraha Sumarna Sumarna Syajili, Ahmad Syukrul Hamdi Tety Dwi Setyaningsih Torang Siregar Toto Sukisno Triutami, Ratna Umi Nurofi’atin Wahyuningsih, Nurul Weni Safitri Widha Nur Shanti Widodo Widodo Zulfa Lailaturrosidah ‎, Haryanto