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All Journal LaGeografiA Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Elektrika Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) DoubleClick : Journal of Computer and Information Technology Journal on Education Journal of Community Development Jurnal Sistem Informasi Jurnal Penelitian Pendidikan
Anan Nugroho
Program Studi Pendidikan Geografi, Universitas Negeri Semarang
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Education Electrical & Electronics Engineering Engineering Nursing Physics Public Health Social Sciences Other

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An Innovative Approach to Automatically Generating Derivative Questions for Calculus with Rule-based Algorithmic Model Trisnani Widowati; Septian Eko Prasetyo; Anan Nugroho; Ade Novi Nurul Ihsani; Alfian Ardhiansyah; Ajeng Rahma Sudarni; Mulil Khaira; Clarita Apriliani; Anik Maghfiroh
Jurnal Penelitian Pendidikan Vol. 42 No. 2 (2025): October 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jpp.v42i2.62082

Abstract

This study aims to develop an automated system for generating derivative function questions to enhance learning and assessment in calculus education. The system uses a rule-based algorithmic approach to generate questions covering various derivative rules, including constant, power, sum, difference, product, quotient, and chain rules, with randomly generated coefficients and exponents. The system's performance was evaluated by measuring the accuracy of the generated questions, achieving a validity rate of 95.63%, and execution time, with an average of 0.00115 milliseconds per question, indicating high efficiency and effectiveness. The results show that the system successfully generates valid questions across various derivative rules, offering significant contributions to both students and educators in the context of calculus education. The system can be further developed by adding additional derivative rules and integrated into larger educational platforms to support a more comprehensive learning experience.
Implementasi Multilayer Perceptron Artificial Neural Network untuk Prediksi Konsumsi Energi Listrik PT PLN (Persero) UP3 Salatiga Roni Saputra; Said Sunardiyo; Anan Nugroho; Subiyanto Subiyanto
Elektrika Vol. 15 No. 2 (2023): October 2023
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/elektrika.v15i2.6411

Abstract

Electricity is energy that flows through cable networks and has become an important part of the progress of human civilization in various fields. The high demand for electrical energy for consumers requires providers of electrical energy to provide a reliable but economical supply of electrical energy. Therefore, strategies and methods are needed to adjust the supply and demand of electrical energy. This can be achieved by carrying out proper and appropriate operational planning. One of the important steps in planning the operation of an electric power system is predicting the demand for electrical energy. However, in the existing research there are still deficiencies in the form of a high error rate. The purpose of this study was to determine the implementation of the multilayer perceptron artificial neural network to predict electricity in 2022-2026 at PT PLN (Persero) UP3 Salatiga. The study used time series data on electricity consumption for the previous 5 years. Based on the research that has been done, the best network variation is TRAINGDA 4 hidden layer with 20 hidden layer nodes, this network model at the training stage produces output with MAD of 2,624,072 kWh and MAPE of 2.79%, and at the stage testing produced an output with MAD of 3,728,386 kWh and MAPE of 3.24%. Keywords: Multilayer perceptron artificial neral network, Forecasting, Electricity consumption.ABSTRAK  Listrik merupakan energi yang mengalir melalui jaringan kabel serta sudah menjadi bagian yang penting dalam kemajuan peradaban manusia di berbagai bidang. Tingginya kebutuhan energi listrik pada konsumen mengharuskan penyedia energi listrik menyediakan suplai energi listrik yang handal tetapi tetap ekonomis. Oleh karena itu, diperlukan strategi dan metode untuk penyesuaian antara supplay dan demand energi listrik. Hal tersebut dapat dicapai dengan melakukan perencanaan operasi yang baik dan tepat, salah satu langkah perencanaan operasi sistem tenaga listrik yang penting yaitu prediksi kebutuhan energi listrik. Namun dalam penelitian yang ada masih terdapat kekurangan berupa tingkat kesalahan yang masih cukup tinggi. Tujuan dari penelitian ini adalah untuk mengetahui implementasi multilayer perceptron artificial neural network untuk melakukan prediksi listrik pada tahun 2022-2026 pada PT PLN (Persero) UP3 Salatiga. Penelitian menggunakan data time series konsumsi energi listrik 5 tahun sebelumnya. Berdasarkan penelitian yang telah dilakukan didapatkan variasi jaringan terbaik yaitu TRAINGDA 4 hidden layer dengan 20 node hidden layer, model jaringan ini pada tahap training menghasilkan output dengan nilai MAD sebesar 2,624,072 kWh dan MAPE sebesar 2.79%, serta pada tahap testing menghasilkan output dengan nilai MAD sebesar 3,728,386 kWh dan MAPE sebesar 3.24%.
Co-Authors Ade Novi Nurul Ihsani Agung Budiwirawan Agus Ardiyanto Agus Suryanto Ahmad Faesal Mubarizi Ahmad Zein Al Wafi Ajeng Rahma Sudarni Akmal Fikri Alfa Faridh Suni Alfian Ardhiansyah Anik Maghfiroh Birawa Kaca Buana Gora Clarita Apriliani Dwi Oktaviyanti Dyah Ratna Salima Fajar Adie Nugraha Gede Aswin Yoga Putra Hanna Naili Syifa' Hari Wibawanto Jhonatur Stheven Simanjuntak Ledi Diyanasari Lestarina Estifani Pradiny Mona Subagja Muhamad Febrian Soambaton Muhamad Novan Aulia Zam Zami Muhammad Fathurrahman Muhammad Febrian Soambaton Mulil Khaira Najma Aulia Nugroho Tegar Maulana Rizal Rezianto Roni Saputra Said Sunardiyo Septian Eko Prasetyo Sri Sukamta Subiyanto Subiyanto Subiyanto Syahroni Hidayat Tri Agus Wahyudi Trida Ridho Fariz Trisnani Widowati, Trisnani Uswatun Hasanah Wafi, Ahmad Zein Al