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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang BAREKENG: Jurnal Ilmu Matematika dan Terapan JITK (Jurnal Ilmu Pengetahuan dan Komputer) Variance : Journal of Statistics and Its Applications Buletin Ilmiah Sarjana Teknik Elektro Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Statistika dan Aplikasinya Journal Of Human And Education (JAHE) Journal of Data Insights Jurnal Pengabdian Masyarakat dan Riset Pendidikan Scientific Journal of Computer Science
Ariska Fitriyana Ningrum
Universitas Muhammadiyah Semarang
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Journal : Journal of Data Insights

 1 
Klasifikasi Dataset Diabetes menggunakan Algoritma K-Nearest Neighbors Musa, Fitri Diana; M. Al Haris; Purwanto, Dannu; Amri, Saeful; Fadlurohman, Alwan; Fitriyana Ningrum, Ariska
Journal of Data Insights Vol 2 No 1 (2024): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v2i1.201

Abstract

Data mining merupakan suatu metode yang baik untuk menangani data skala besar. Performasi menjadi penting dalam metode data mining. Salah satu metode yang memiliki performasi terbaik adalah K-Nearest Neighbor (KNN). Artikel ini membahas terkait performasi K-NN. Data yang digunakan pada penelitian ini adalah Diabetes. Data dibagi menjadi 80% data trainingdan 20% data testing. Dengan menggunakan 11 tetangga terdekat, model menghasilkan akurasi sebesar 0.765625. Angka ini mencerminkan kinerja yang baik. Metrik kritis termasuk akurasi sebesar 0.77, presisi sebesar 0.80, dan recall sebesar 0.85. Hasil ini menunjukkan bahwa model KNN memiliki potensi untuk mengklasifikasikan pasien diabetes dengan akurasi yang baik.
DASHBOARD LINGKUNGAN HIDUP UNTUK ANALISIS DIARE MENGGUNAKAN METODE K-MEANS CLUSTERING Sitti Sahara; Amri, Saeful; Fitriyana Ningrum, Ariska; Purwanto, Dannu
Journal of Data Insights Vol 2 No 1 (2024): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v2i1.210

Abstract

Abstrak Singkat: Diare adalah penyakit umum dengan penyebab yang beragam, termasuk virus, bakteri, dan faktor-faktor lainnya. Faktor-faktor lingkungan, gizi yang buruk, dan kurangnya pengetahuan masyarakat berperan penting dalam tingginya kasus diare, terutama pada anak-anak di bawah lima tahun, di Indonesia. Analisis cluster digunakan untuk mengelompokkan daerah berdasarkan kasus diare dan membantu perencanaan penanggulangan. Penelitian ini menggunakan data BPS 2021 dari 34 provinsi di Indonesia dan berfokus pada faktor penyebab diare. Penelitian ini bertujuan untuk memahami faktor-faktor yang berkontribusi pada kasus diare, dengan harapan dapat merumuskan strategi penanggulangan yang lebih efektif.
Fuzzy Gustafson Kessel for Infrastructure Development Strategy in South Sumatra Province: Fuzzy Gustafson Kessel Untuk Strategi Pembangunan Infrastruktur Di Provinsi Sumatera Selatan Ningrum, Ariska Fitriyana; Rahma Dhani, Oktaviana; Anggun Lestari, Febi; Aura Hisani, Zahra; Fadlurohman , Alwan
Journal of Data Insights Vol 2 No 2 (2024): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v2i2.650

Abstract

Infrastructure development is a strategic element in improving public services and economic growth. South Sumatra Province, with its large economic potential, faces challenges in managing efficient and sustainable infrastructure development. This research aims to apply the Fuzzy Gustafson Kessel (FGK) method in decision making related to infrastructure development in South Sumatra Province. FGK combines fuzzy logic with Gustafson Kessel clustering algorithm to handle uncertainty and data variation from various stakeholders. The data used in this study includes population and geographic census data from the Central Bureau of Statistics of South Sumatra Province in 2023, with five indicators: population, area, population growth rate, population density, and poverty rate. The results show that South Sumatra is divided into three main clusters based on its infrastructure and demographic characteristics. This clustering is expected to improve the effectiveness and efficiency of infrastructure development decision-making, provide more appropriate policy recommendations, and potentially be applied in other regions with similar challenges.
Evaluation of Deep Learning Optimizers for Predicting JISDOR Exchange Rates Using LSTM Networks: Evaluasi Pengoptimalan Deep Learning untuk Memprediksi Nilai Tukar JISDOR Menggunakan Jaringan LSTM Ningrum, Ariska Fitriyana; Purwanto, Dannu; Kusuma Wardani, Amelia
Journal of Data Insights Vol 3 No 1 (2025): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v3i1.726

Abstract

This research explores the application of four optimization algorithms—Adam, Nadam, RMSProp, and SGD—on a Long Short-Term Memory (LSTM) model to forecast the Jakarta Interbank Spot Dollar Rate (JISDOR). The volatile nature of exchange rate data, influenced by global and domestic economic dynamics, necessitates the use of models like LSTM that excel in capturing both short- and long-term dependencies. Performance was assessed using metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Among the optimizers, Nadam proved to be the most effective, achieving the lowest RMSE of 62.767 and a MAPE of 0.003, indicating its capability in managing complex fluctuations in the dataset. Despite Nadam's promising results, opportunities for improvement remain, including the inclusion of additional input variables, fine-tuning model parameters, and expanding the training dataset. This study underscores the critical role of selecting appropriate optimization algorithms for enhancing the accuracy of LSTM models in forecasting volatile financial time-series data, particularly for currency exchange rates
Co-Authors Alfian Ma’arif Ali Imron Alwan Fadlurohman Amri, Saeful Anggun Lestari, Febi Asno Azzawagama Firdaus Aura Hisani, Zahra Bisoumi, Yan Nazala Dannu Purwanto Daud, Khamidah Arsyad Dhani, Oktaviana Rahma Dwi Sulistiani Furizal, Furizal Ihsan Fathoni Amri Kariyamin, Kariyamin Khaira, Mulil Kusuma Wardani, Amelia M. Al Haris Mostfa, Ahmed A. Muhammad Ivan Ardiansyah Musa, Fitri Diana mutiah, siti Nakib, Arman Mohammad Nisa, Rofiah Ainun Nudyawati, Elsa Nurhidayah, Sri Prizka Rismawati Arum Rahma Dhani, Oktaviana Rohim, Febrian Hikmah Nur Saputra, Farid Sam Sharkawy, Abdel Nasser Sitti Sahara Supriyanto Talirongan, Florence Jean B. Tangaro, Diana May Glaiza G. Tiani Wahyu Utami Wardani, Amelia Kusuma Wijaya, Setiawan Ardi Yunanita, Novia Yusrin, Yusrin Zahra Aura Hisani