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All Journal Jurnal Simetris Jurnal Teknologi dan Sistem Komputer JURNAL ENGGANO Jurnal DISPROTEK JURTEKSI Abdimas Universal Jurnal Teknologi Dan Sistem Informasi Bisnis SKANIKA: Sistem Komputer dan Teknik Informatika Bulletin of Computer Science Research Journal of Dinda : Data Science, Information Technology, and Data Analytics As-Sidanah : Jurnal Pengabdian Masyarakat Jurnal Minfo Polgan (JMP) Jurnal Rekognisi Akuntansi (JRA) J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) Science and Technology (SciTech)
Mulyo, Harminto
Universitas Islam Nahdlatul Ulama Jepara
Religion Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Journal : Jurnal DISPROTEK

 1 
STATISTICAL TECHNIQUE DAN PARAMETER OPTIMIZATION PADA NEURAL NETWORK UNTUK FORECASTING HARGA EMAS Harminto Mulyo
Jurnal DISPROTEK Vol 7, No 2 (2016)
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jdpt.v7i2.432

Abstract

ABSTRACT Gold is a precious metal that is valuable in the world that is soft, corrosion-resistant, malleable. The investment experts often advise to invest in gold because gold is a classic hedge against inflation and adds value in conditions of instability of currency exchange rate fluctuations. Gold price history data from year to year tends to rise, it's interesting researchers to examine the data using a variety of forecasting methods, including Statistical Technique and Data Mining Technique. Experiments were performed to look for the smallest error value by using statistical techniques and data mining. From the test results, the best statistical technique that uses a technique Single Moving Average with MSE of 760.55. In the data mining technique using Neural Network Backprogration obtained RMSE of +/- 22 730 6945. Keywords: Parameter, Optimization, gold, Neural Network ABSTRAK Emas merupakan salah satu logam mulia yang bernilai di dunia yang bersifat lunak, tahan korosi, mudah ditempa. Para pakar investasi seringkali menganjurkan untuk berinvestasi pada emas karena emas merupakan sarana lindung nilai klasik untuk melawan inflasi dan menambah nilai dalam kondisi ketidakstabilan fluktuasi nilai mata uang. Data riwayat harga emas dari tahun ke tahun cenderung naik, hal tersebut menarik peneliti untuk menguji data menggunakan berbagai metode peramalan, diantaranya Statistical Technique dan Data Mining Technique.Eksperimen dilakukan untuk mencari nilai error terkecil dengan menggunakan teknik statistik dan data mining. Dari hasil pengujian, teknik statistik terbaik yaitu menggunakan teknik Single Moving Average dengan MSE sebesar 760.55. Pada teknik data mining dengan menggunakan metode Neural Network Backprogration didapat RMSE sebesar 22.730 +/- 6.945. Kata Kunci: Parameter, Optimization, emas, Neural Network
INTEGRATION OF STUDENT ACADEMIC INFORMATION SYSTEMS WITH TELEGRAM BOT AS AN AUTOMATIC ANSWERING MACHINE Harminto Mulyo; Gunawan Mohammad
Jurnal DISPROTEK Vol 13, No 1 (2022)
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jdpt.v13i1.3043

Abstract

The Student Academic Information System (SIAMA) is web-based software developed to manage students' academic data. In this system, student academic data is processed to make academic information useful for students. However, to access the system requires many steps so that time is less efficient if you have to browse through all the stages. Not to mention the need for internet data quotas that are calculated based on how much content the system downloads and uploads. To speed up access to that information, it can be done by integrating the Telegram Bot service as an automated answering service controlled by robots. Using the PPDIOO method, SIAMA has been successfully integrated as an automated answering machine through the utilization and development of the Telegram Bot PHP Library collaborated with Crontab Linux. From the test results, it is known that the response time from the Bot is indicated at the same hour and minute or < 1 minute which means that the bot execution time is relatively fast.
PENINGKATAN AKURASI PREDIKSI PEMILIHAN PROGRAM STUDI CALON MAHASISWA BARU MELALUI OPTIMASI ALGORITMA DECISION TREE DENGAN TEKNIK PRUNING DAN ENSEMBLE Mulyo, Harminto; Maori, Nadia Annisa
Jurnal Disprotek Vol 15, No 1 (2024)
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jdpt.v15i1.5585

Abstract

ENHACING PREDICTION ACCURACY OF NEW STUDENT PROGRAM SELECTION THROUGH DECISION TREE ALGORITHM OPTIMIZATION WITH PRUNING TECHNIQUE AND ENSEMBLEIn the current era of reform and globalization, the complexity of choosing the right study program is increasing with the many choices available. One of the challenges faced by the Nahdlatul Ulama Islamic University (UNISNU) Jepara is the increase in students with non-active status which can have an impact on the reputation of the university. One of the factors that can influence is the inaccuracy of students in choosing a study program, so that they are reluctant to continue because they are not enthusiastic about continuing their studies. The solution provided is to predict the selection of the right study program for prospective new students by utilizing the Decision Tree algorithm which is optimized with pruning and ensemble techniques with Random Forest which can help overcome overfitting in the decision tree. The data used is UNISNU student data from 2013 to 2023 with a total of 15,289 records and 52 attributes. The results showed that the Decision Tree and Random Forest models provided the highest accuracy, namely 0.88 with a max_depth value of 20 and succeeded in overcoming the problem of overfitting the decision tree. This model can then be used as a recommendation in predicting the selection of study programs for prospective new students at UNISNU Jepara.Dalam era reformasi dan globalisasi saat ini, kompleksitas dalam memilih program studi yang sesuai semakin meningkat dengan banyaknya pilihan yang tersedia. Salah satu tantangan yang dihadapi oleh Universitas Islam Nahdlatul Ulama (UNISNU) Jepara adalah meningkatnya mahasiswa dengan status non-aktif yang dapat berdampak pada reputasi universitas. Salah satu faktor yang dapat mempengaruhi adalah ketidaktepatan mahasiswa dalam memilih program studi, sehingga enggan untuk meneruskan karena tidak bersemangat dalam melanjutkan perkuliahan. Solusi yang diberikan adalah dengan melakukan prediksi pemilihan program studi bagi yang tepat bagi calon mahasiswa baru dengan memanfaatkan algoritma Decision Tree yang dioptimalkan dengan teknik pruning dan ensemble dengan Random Forest yang dapat membantu mengatasi overfitting pada decision tree. Data yang digunakan adalah data mahasiswa UNISNU dari tahun 2013 sampai dengan 2023 dengan jumlah 15.289 record dan 52 atribut. Hasil penelitian menunjukkan model Decision Tree dan Random Forest memberikan akurasi tertinggi, yaitu 0.88 dengan nilai max_depth sebesar 20 dan berhasil mengatasi masalah overfitting pada decision tree. Model ini selanjutnya dapat menjadi rekomendasi dalam prediksi pemilihan program studi bagi calon mahasiswa baru di UNISNU Jepara.
Co-Authors Abdilllah, Muh Sajid Akhmad Khanif Zyen Ali Sofwan An-Naas Shahifatun Na’iema Andriansyach, Dimas Jordy Anita Ameliya Anwar, Muhammad Arqom Arif Mustofa Aufa Maulana Hidayat Azhar, Maulana Buang Budi Wahono, Buang Budi Doni Agung Prasetyo Dwi Noviyani Fira Nadliratul Afrida Gunawan Mohammad Hayu Dian Yulistianti Irma Nur Af’idah Kartika Widiastuti Khalimatus Sa'diyah Manzis, Akhmad Hossam Maori, Nadia Annisa Mochamad Handre Fidiyanto Muhammad Misbahul Munir Muhammad Verdy Fahrudin Nadia Annisa Maori Nur Aeni Widiastuti Nur Aeni Widiastuti Nur Taqwimi, Muhammad Ahsani Nusrotus Sa’idah Octavianti Paramitha R Hadapiningradja Kusumodestoni Rahmawan Arniyanto Rivaldo, Muchammad Dwi Rohmatun Nikmah Santi Andriyani Sarwido Sarwido, Sarwido Teguh Tamrin Wibowo, Gentur Wahyu Nyipto Zachary, Vivien Varian Zubaedah Zubaedah