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All Journal Indonesian Journal of Artificial Intelligence and Data Mining BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Pengembangan Rekayasa dan Teknologi Jurnal Ilmiah Pendidikan dan Pembelajaran Soeropati: Journal of Community Service Journal of Data Insights Prosiding Seminar Nasional Unimus
Indah Manfaati Nur
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Religion Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

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A Pengelompokan Kabupaten/Kota di Jawa Tengah Berdasarkan Kepadatan Penduduk Menggunakan Metode Hierarchical Clustering Yusrisma Asyfani; Indah Manfaati Nur; Ihsan Fathoni Amri; Novia Yunanita; Febi Anggun Lestari; Zahra Aura Hisani; Febrian Hikmah Nur Rohim
Journal of Data Insights Vol 2 No 1 (2024): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

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Abstract

Jawa Tengah merupakan provinsi dengan urutan kelima di Indonesia berdasarkan kepadatan penduduk pada tahun 2020 sebanyak 1.113 jiwa/km2. Pengaruh kepadatan penduduk yang tinggi dapat menyebabkan berbagai masalah diantaranya kemacetan,pengangguran,kesehatan,kriminalitas serta permasalahan serius lainnya. Kepadatan penduduk dipengaruhi oleh angka kelahiran,angka kematian serta laju pertumbuhan, Untuk mengevaluasi kepadatan penduduk di provinsi Jawa Tengah, kita perlu mengklasifikasikan/mengelompokkan kabupaten/kota yang berada didalamnya. Pengelompokan ini bertujuan agar kebijakan yang dibuat oleh pemerintah dapat tepat sasaran. Metode yang dapat digunakan untuk pengelompokkan kabupaten.kota di provinsi Jawa Tengah berdasarkan kepadatan penduduknya yaitu Clustering Hierarchical Ward. Dari hasil analisis pengelompokan tersebut kabupaten/kota di provinsi Jawa Tengah dibagi menjadi empat kelompok berdasarkan kepadatan penduduknya.
Analysis of Social Vulnerability in Java Island using K-Medoids Algorithm with Variation of Distance Measurements (Euclidean, Manhattan, Minkowski) Nur, Indah Manfaati; Abdurakhman, Abdurakhman
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.31111

Abstract

The Social Vulnerability Index (SoVI) measurement to assess social vulnerability is only able to describe conditions in general, without being able to show which factors dominate the score. Therefore, the aim of this research is to fill this gap by applying a correlational approach with a clustering method to characterize the dominant factors of social vulnerability at the district level in Java and surrounding areas. The clustering method used in this study is the K-Medoids algorithm. This method is more powerful when there are outliers in the dataset used. In this study, we considered the use of 3 different distance methods, namely Euclidean distance, Manhattan distance, and Minkowski distance. As a result, the K-Medoids algorithm using Manhattan distance provides the best value based on the Davies Bouldin Index. This research found that social vulnerability exists in every region of Java Island and its surroundings.
Metode Bidirectional Long Short-Term Memory (BiLSTM) Untuk Memprediksi Harga Saham BBRI Dengan Optimasi Nesterov Adaptive Moment (Nadam) Permatasari, Shella Heidy; Nur, Indah Manfaati; Fauzi, Fatkhurokhman
Prosiding Seminar Nasional Unimus Vol 7 (2024): Transformasi Teknologi Menuju Indonesia Sehat dan Pencapaian Sustainable Development G
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Prediksi harga saham merupakan salah satu bidang yang sangat menantang dan memiliki dampak signifikandalam dunia keuangan. Penelitian ini menggunakan metode Bidirectional Long Short-Term Memory(BiLSTM) yang dioptimasi dengan Nesterov Adaptive Moment (Nadam) untuk memprediksi harga sahamharian PT Bank Rakyat Indonesia (BBRI). Metode BiLSTM merupakan variasi dari metode Long ShortTerm Memory (LSTM) yang memecahkan ketergantungan jangka pajang LSTM dengan RNN (RecurrentNeural Network). BiLSTM ini memiliki kemampuan untuk menangkap pola temporal dari masa lalu danmasa depan sehingga efektif dalam analisis deret waktu. Sedangkan, optimasi Nadam digunakan untukmeningkatkan kecepatan konvergensi dan akurasi prediksi dengan memanfaatkan kelebihan darimomentum Nesterov dan adaptivitas Adam. Metode BiLSTM yang dioptimalkan dengan optimasi Nadamtersebut menghasilkan model prediksi harga saham harian terbaik dengan konfigurasi optimal 30 neuronper lapisan tersembunyi, batch size 256, dan 500 epoch. Konfigurasi ini efektif dalam menangkap polafluktuasi harga saham harian dengan MSE 0,000415. Pada evaluasi kinerja model dengan MAPE, diperolehnilai MAPE 1,7511% pada data training serta 1,5432% pada data testing, yang menunjukkan bahwa modeltersebut akurat dengan tingkat kesalahan prediksi di bawah 10%. Hasil prediksi menunjukkan kenaikanstabil setiap hari dengan harga saham terendah 4496,028 pada 1 Juni 2024 dan tertinggi 4819,317 pada 29Juni 2024. Kata Kunci : BiLSTM, Nesterov Adam, Prediksi, Saham
Optimizing the Role of Higher Education in Increasing the Response Rate of the Online Population Census 2020 in Semarang: Optimalisasi Peran Perguruan Tinggi dalam Meningkatkan Respon Rate Sensus Penduduk Online 2020 di Semarang Nur, Indah Manfaati; Al Haris, M.; S.A.P., Rangga Sa'adillah; Hasyim, Mochamad
Jurnal Soeropati Vol 5 No 2: Mei 2023
Publisher : LPPM Universitas Yudharta Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35891/js.v5i2.3908

Abstract

The online population census was first launched in 2020. The purpose of the online population census 2020 is to provide data on the number, composition, distribution, and characteristics of the Indonesian population towards one Indonesian population data and provide demographic parameters and population projections and other population characteristics for population projections and SDGs indicators. These data are needed by the government as one of the bases for making decisions or policies in order to be able to accommodate all existing interests. This innovation with an online census approach is undoubtedly inseparable from social problems or constraints. Social, economic, and geographic factors affect the literacy of information and communication technology in society. The factual conditions in the field encouraged the team community service to take a strategic role by carrying out community service activities in Kecamaran Pabelan, Semarang Regency, in the form of online population census 2020 assistance activities. Mentoring methods are carried out by providing counseling, socialization, and technical guidance to the Public. The results achieved from this assistance to partners were an increase in the community response rate in Semarang Regency, more partners could participate and it was easier to fill data in the online population census 2020.
The Impact of Implementing the Independent Curriculum on Elementary School Students' Learning Outcomes Fisabilillah, Muh. Irodat; Ahmadi; Supriadin; Ridwanulhaq, Alfina Fauziah; Masudah, Nurhidayatul; Nur, Indah Manfaati; Haris, M. Al; Amri, Saeful
Jurnal Ilmiah Pendidikan dan Pembelajaran Vol. 9 No. 1 (2025): March
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jipp.v9i1.91548

Abstract

The Independent Curriculum is an essential component in Muhammadiyah Elementary Schools in Semarang, supporting the spirit of learning that has developed over time. However, there is an imbalance in student achievement scores between phases that require data-based tracking. This study aims to evaluate the effectiveness of the Independent Curriculum in improving students' cognitive understanding using the factorial design method. This type of research is quantitative descriptive. The research population is Muhammadiyah Elementary Schools in the independent school category, with a sample of four schools selected using the two-stage cluster random sampling technique. Data collection techniques are observation and questionnaires. The application of analysis methods includes descriptive and inferential statistics. The research results show that the implementation of the Independent Curriculum can significantly improve students' cognitive understanding, as reflected in the increase in the average student achievement scores between the 2022/2023 and 2023/2024 academic years. In addition, the analysis of the elementary school phase shows that the Independent Curriculum can support the development of student competencies in stages according to educational needs in each phase, thereby improving the quality of learning.
FORECASTING THE NUMBER OF FOREIGN TOURISM IN BALI USING THE HYBRID HOLT-WINTERS-ARTIFICIAL NEURAL NETWORK METHOD Haris, M. Al; Himmaturrohmah, Laily; Nur, Indah Manfaati; Ayomi, Nun Maulida Suci
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp1027-1038

Abstract

Bali was one of the destinations frequently visited by tourists because it had natural beauty, especially in the tourism sector. The number of foreign tourists coming to Bali until 2019 had increased, but there had been a very significant decrease in 2020. Forecasting the number of tourists coming to Bali in the future was needed to provide input or recommendations to the government and business people in anticipating decisions taken in the process of developing the tourism sector in Bali. One of the forecasting methods that can be used was the Holt-Winters method. The Holt-Winters method was part of Exponential Smoothing which is based on smoothing stationary, trend and seasonal elements. However, the Holt-Winters method can only capture linear patterns, so a method was needed that can capture non-linear patterns. The Artificial Neural Network method was proposed to overcome the shortcomings of the Holt-Winters Method. This research was focused on the number of foreign tourists visiting Bali using the Hybrid Holt Winters-Artificial Neural Network method. The results showed that the data on the number of foreign tourists fluctuated every month. The best method for predicting the number of foreign tourists was the Hybrid Holt-Winters (α = 0.987, β = 0.000001, and γ = 1)-Artificial Neural Network (12-15-1) because it has the best accuracy as indicated by the MAD value of 0.036684, MSE 0.01098698 and MAPE 6.30417%.
CRYPTOCURRENCY PRICE PREDICTION: A HYBRID LONG SHORT-TERM MEMORY MODEL WITH GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY Nur, Indah Manfaati; Nugrahanto, Rifqi; Fauzi, Fatkhurokhman
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1575-1584

Abstract

Cryptocurrency is a virtual payment instrument currently popular as an investment alternative. One type of cryptocurrency widely used as an investment is Bitcoin due to its high-profit potential and risk due to unstable exchange rate fluctuations. This high exchange rate fluctuation makes trading transactions in the crypto market speculative and highly volatile. To overcome this volatility factor, this research used the Generalized Autoregressive Conditional Heteroscedasticity forecasting method to describe the heteroscedasticity factor, as well as a Recurrent Neural Network (RNN) with long-short-term memory that has feedback in modeling sequential data for time series analysis. The two methods are combined to overcome the dependency of time series data in the long term and the heteroscedastic effect of the volatility of price changes. The results of the GARCH-LSTM hybrid model in this study show a Mean Absolute Percentage Error (MAPE) value of 15.69%. The accuracy value is obtained from the division of training data by 80% and testing data by 20%, with the number of neurons as many as three and epochs of 100 using the Adam optimizer. The MAPE accuracy results show a good prediction in predicting the value.
Pengelompokan Wilayah Kecamatan di Kabupaten Kendal Berdasarkan Hasil Produksi Buah dan Sayur Dengan Metode K-means Clustering Arum, Prizka Rismawati; Nur, Indah Manfaati; Fitriyani, Indah; Amri, Saeful
Jurnal Pengembangan Rekayasa dan Teknologi Vol. 7 No. 1 (2023): Mei (2023)
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/jprt.v19i1.8212

Abstract

Indonesia dikenal dengan sebutan negara agraris dimana sebagian besar penduduk bekerja di sektor produksi pertanian. Kabupaten Kendal merupakan salah satu kabupaten di Provinsi Jawa Tengah yang sebagian besar wilayahnya merupakan daerah produksi pertanian yang sangat subur. Data yang digunakan dalam kasus ini adalah hasil produksi produksi pertanian buah dan sayur pada 20 kecamatan di Kabupaten Kendal tahun 2022. Salah satu cara untuk mengetahui potensi produksi pertanian dari wilayah kecamatan di Kabupaten Kendal adalah dengan mengelompokkan wilayah yang memiliki karakteristik hampir sama menggunakan K-means clustering. Tujuannya adalah mendapatkan hasil pengelompokkan yang optimal dari masing-masing kelompok yang terbentuk. Berdasarkan hasil analisis, diperoleh pengelompokkan wilayah kecamatan di Kabupaten Kendal menggunakan K-means menjadi 3 cluster. Dimana Klaster 1 terdiri dari 2 kecamatan dengan identifikasi bawang merah, mangga, pisang, dan jambu air memiliki tingkat persentase hasil produksi buah dan sayuran tertinggi. Klaster 2 terdiri dari 2 kecamatan dengan identifikasi pepaya, nangka, petai, dan melinjo memiliki tingkat persentase hasil produksi buah dan sayuran tertinggi. Dan klaster 3 terdapat 16 kecamatan dengan identifikasi cabai rawit, cabai keriting, memiliki tingkat persentase hasil produksi buah dan sayuran tertinggi. Dengan nilai evaluasi yang didapatkan dari Silhouette Index sebesar 0,5546 yang berarti termasuk kedalam kriteria medium structure.
Panel Data Spatial Regression Modeling with a Rook Contiguity Weighting Function on the Human Development Index in West Sumatera Province Arum, Prizka Rismawati; Anggraini, Lisa; Nur, Indah Manfaati; Purnomo, Eko Andy
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 1 (2024): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i1.16675

Abstract

The achievement of the level of welfare of a region or country can be seen from the level of human development as measured by the Human Development Index (HDI). West Sumatra is one of the provinces with HDI achievements above the national average. However, there are still regencies/cities in West Sumatra Province that have HDI achievements below the national average and HDI achievements in West Sumatra Province Regencies/Cities have changed in 2017-2021. Therefore, in this study, spatial analysis of panel data was used. The aim of this research is to find out the general description of the HDI of West Sumatra Province, obtain a panel data spatial regression model and obtain variables that significantly influence on HDI in West Sumatra Province 2017─2021because differences in HDI achievement were suspected to have influences from areas that were side by side and the area was observed more than once. The model formed from this analysis using the rook contigutiy weighting function is Random Effect Spatial Autoregressive because the spatial interactions formed in human development index data in West Sumatra Province are real at lag. This model is a suitable model based on panel spatial model selection and has an R2 value of 92.94%. Analysis of human development index data in regencies/cities in West Sumatra Province using spatial regression panel data obtained results that expectations of school length (X1), average length of schooling (X2), and population density (X3) significantly directly influenced the human development index in regencies/cities in West Sumatra Province.  
Co-Authors Abdurakhman Abdurakhman AHMADI Al Haris, M. Amri, Saeful Ayomi, Nun Maulida Suci Eko Andy Purnomo Fauzi, Fatkhurokhman Febi Anggun Lestari Febrian Hikmah Nur Rohim Fisabilillah, Muh. Irodat Haris, M. Al Himmaturrohmah, Laily Ihsan Fathoni Amri Indah Fitriyani Lisa Anggraini Masudah, Nurhidayatul Mochamad Hasyim Novia Yunanita Nugrahanto, Rifqi Permatasari, Shella Heidy Prizka Rismawati Arum Ridwanulhaq, Alfina Fauziah S.A.P., Rangga Sa’adillah Supriadin Supriadin Yusrisma Asyfani Zahra Aura Hisani