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All Journal Jurnal Simantec Jurnal Ilmiah Kursor ELINVO (Electronics, Informatics, and Vocational Education) Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) Network Engineering Research Operation [NERO] Jambura Journal of Mathematics ComTech: Computer, Mathematics and Engineering Applications Journal of Information Systems and Informatics JoMMiT : Jurnal Multi Media dan IT JUSIFOR : Jurnal Sistem Informasi dan Informatika International Journal of Integrative Sciences Signal and Image Processing Letters
Fifin Ayu Mufarroha
Fakultas Teknik, Universitas Trunojoyo Madura
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences

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Implementation of the Naive Bayes and Simple Additive Weighting Methods in the Feasibility Selection of Village Cash Assistance Recipients Bain Khusnul Khotimah; Wahyu Indra Kustina; Yeni Kustiyahningsih; Devie Rosa Anamisa; Fifin Ayu Mufarroha
International Journal of Integrative Sciences Vol. 2 No. 5 (2023): May, 2023
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/ijis.v2i5.4244

Abstract

Village Fund Direct Cash Assistance (VF-DCA) is a direct cash assistance policy in which funds come from the village. The VF-DCA policy is expected to ease the burden on the community, especially those with a low economy. Still, it is undeniable that there are opportunities for misuse of village funds by some VF-DCA organizers, intentionally or unintentionally. Problems that arise in implementing VF-DCA are mistakes in determining the beneficiary communities that are not on target so that the impact of VF can be manipulated for the interests of certain groups in several cases of social assistance programs. The solution to these problems is that this research creates a decision support system in selecting the eligibility of assistance recipients. This research used the Naïve Bayes (NB) method in selecting VF-DCA recipients and the Simple Additive Weighting (SAW) way to determine the order of eligibility scores for each candidate for VF-DCA recipients. In this study, the results of the classification process using the NB method obtained an accuracy rate of 99.36%. Hence, this is simplified in determining potential recipients of VF-DCA using the NB and SAW methods
Prediction Model for Tourism Object Ticket Determination in Bangkalan, Madura, Indonesia Fifin Ayu Mufarroha; Akhmad Tajuddin Tholaby; Devie Rosa Anamisa; Achmad Jauhari
ComTech: Computer, Mathematics and Engineering Applications Vol. 14 No. 2 (2023): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v14i2.7992

Abstract

One of the regencies in Madura, namely Bangkalan, with its local wisdom and beautiful landscapes has the potential to become a tourism center. However, there may be a decrease in the number of visits caused by some factors. The research used the time series method to build a prediction model for tourist attraction entrance tickets. The model development aimed to estimate the number of tourist attraction visits in the future. The right model was needed to get the best prediction results. Least square, Holt-Winter, Seasonal Autoregressive Integrated Moving Average (SARIMA), and Rolling were chosen as the models. Data collection related to the number of tourist objects was carried out directly at the Tourism Office to obtain valid data. Using data on visitors to tourist attractions in Bangkalan Regency from 2015 to 2019, the results of measuring errors using Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) are obtained. The error measurement results show that the Holt-Winter model has the lowest error rate of 5% and RMSE of 307,1198. Based on these calculations, the Holt-Winter model is the best model for determining tourist attraction entrance tickets. The ranking of the error measurement results from the highest to the lowest are Holt-Winter, Rolling, SARIMA, and Least Square methods.
Prediksi Jumlah Penderita Stunting di Madura Dengan Pendekatan Machine Learning Septiyanto, Triyas; Mufarroha, Fifin Ayu; Anamisa, Devie Rosa; Jauhari, Achmad
JoMMiT Vol 7 No 2 (2023): Artikel Jurnal Volume 7 Issue 2, Desember 2023
Publisher : Politeknik Negeri Media Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46961/jommit.v7i2.901

Abstract

Indonesia, sebagai negara kepulauan terbesar di dunia dengan luas daratan hampir tiga ribu pulau, memiliki potensi ekonomi signifikan dan sumber daya unik. Namun, Indonesia masih menghadapi tantangan serius dalam bidang kesehatan gizi, terutama terkait dengan stunting, kurang gizi, dan obesitas. Stunting adalah kondisi gizi yang mengindikasikan tinggi badan seseorang tidak sesuai dengan usianya, dan memiliki dampak jangka pendek dan panjang terhadap kualitas hidup individu. Untuk mengatasi masalah kesehatan ini, perlu diterapkan metode inovatif seperti teknologi machine learning. Penelitian ini bertujuan untuk memprediksi jumlah penderita stunting di Madura, Dengan menggunakan metode Least Square, dengan nilai Evaluasi akurasi peramalan MAD sebesar 475, MSE sebesar 1128125, dan MAPE sebesar 15.56%, menunjukkan bahwa model yang dikembangkan memiliki kemampuan yang baik dalam memprediksi jumlah penderita stunting di Madura.
Development of A Tourist Destination Object Search Application as A Madura Tourism Information Media Using ADDIE Model Fatah, Doni Abdul; Suzanti, Ika Oktavia; Ifandia, Alfian Mahendra; Negara, Yudha Dwi Putra; Mufarroha, Fifin Ayu
Journal of Information System and Informatics Vol 6 No 1 (2024): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i1.685

Abstract

The rapid development of tourism has caused many tourists to be interested in enjoying it. Madura Island is a small archipelago and is part of East Java. Madura consists of 4 districts, namely Bangkalan district, Sampang district, Pamekasan district, and Sumenep district. With the number of districts owned, it creates a diversity of tourism. The development of the tourism sector must also be accompanied by the development of information technology. Utilization of technology can improve accommodation in supporting tourist destination services by designing an android-based application for searching for objects supporting Madura tourism destinations. The purpose of this research is to create an object search application that supports tourist destinations and serves as a medium for information about tourist locations on the island of Madura, making it more straightforward for tourists/users to enjoy their vacation. The stages of research carried out using ADDIE Model include knowing the system requirements, analysis and design, Development, implementation, and Evaluation. The results of the study are in the form of applications by detecting supporting objects including places of interest, gas stations, lodging, religion, culinary and health. It is expected that users when visiting Madura tourism will be easy, fun, and can enjoy their holidays.
Visitor Decision System in Selection of Tourist Sites Based on Hybrid of Chi-Square And K-NN Methods Anamisa, Devie Rosa; Mufarroha, Fifin Ayu; Jauhari, Achmad
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 8 No. 2 (2023): November 2023
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v8i2.55702

Abstract

Madura Island is one of the islands with a lot of tourism spread over four districts, such as natural, religious, and cultural tourism. And every year, various visitors visit various tourist sites in Madura, so an increase in the number of visitors has been found in multiple places. This is influenced in addition to the type of tourist attraction but also changes in tourist behavior in making decisions to visit tourist objects. Most of the researchers have applied the right decision-making with intelligence-based measurement. However, the accuracy obtained has not yet reached the optimal solution. Therefore, this study uses the Chi-Square and K-Nearest Neighbors (K-NN) methods to recommend tourist attraction locations based on visitor characteristics to increase visitor attractiveness in tourist attractions scattered in Bangkalan, Madura. Chi-Square is used to select features that affect tourist attraction visitor factors by testing the relationship between the variables involved. Meanwhile, K-NN is a method of classifying potential visitor attractions based on their characteristics by using the closest membership calculation, which is the largest from the test data. The calculation is carried out by the square of the Euclidian distance from each object, then sorted from the smallest to the largest value and looking for the value of k as the result of the decision. There are ten features used in the classification, such as tourism type, management services, facilities, gender, age, occupation, education, visitor status, ticket prices, and sales trends. There are three classes classified: low, medium, and high visitor attractiveness. The contribution of this study is to analyze the effect of the characteristics of tourist attraction visitors on increasing visitor attractiveness using the chi-square and K-NN methods. Based on the results of system testing using K-Fold Cross Validation with five folds from 315 datasets, it produces the highest accuracy at k-fold = 3 worth 84.12% with eight selected features.
Forecasting of Rice Harvest Results Using SVR Modeling Techniques Anamisa, Devie Rosa; Khotimah, Bain Khusnul; Hariyawan, Mohammad Yanuar; Irhamni, Firli; Jauhari, Achmad; Mufarroha, Fifin Ayu; Violina, Dina; Nuraini, Dinah
Jambura Journal of Mathematics Vol 7, No 1: February 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i1.30592

Abstract

Forecasting is an activity that predicts future values {}{}by utilizing existing track record data. The object of this study is rice plants because they are the primary food source for the Indonesian people. Every year, the Government strives for rice farmers throughout Indonesia to produce abundant rice harvests to meet the community's food needs. Therefore, rice farmers need a system that can predict their rice harvests to obtain information about future harvests to find out whether their harvests have decreased or increased so that they can determine efforts that can be made in the future and can be used as a policy maker for the Government in maintaining the national food security chain. This study uses time series data on rice harvests in Pamekasan, Madura, for 2007-2023 using the Support Vector Regression (SVR) model. The results of several trials have shown that the application of the SVR model for forecasting rice harvests in 2024 has produced good accuracy with a relatively low MAPE error rate of 3.97\%, and the rice harvest has reached an average prediction of 15470.08 tons with an average actual data of 7937.884 tons. Therefore, applying this SVR model can be recommended for predicting future rice harvests. 
Prediksi Hasil Panen Padi dengan Metode Multiple Linear Regression dan Particle Swarm Optimization untuk Meningkatkan Produksi Padi di Madura Nuraini, Dinah; Violina, Dina; Anamisa, Devie Rosa; Khotimah, Bain Khusnul; Jauhari, Achmad; Mufarroha, Fifin Ayu
JUSIFOR : Jurnal Sistem Informasi dan Informatika Vol 4 No 1 (2025): JUSIFOR - Juni 2025
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/jusifor.v4i1.5857

Abstract

Pertanian memegang peran penting dalam perekonomian Indonesia, khususnya sektor pertanian padi yang menjadi sumber pangan utama bagi sebagian besar penduduk. Meskipun Indonesia memiliki potensi besar dalam sektor ini, produksi padi seringkali mengalami fluktuasi yang memengaruhi ketahanan pangan. Oleh karena itu, prediksi produksi padi sangat penting untuk merencanakan kebijakan pertanian dan memastikan ketahanan pangan. Metode yang digunakan adalah Multiple Linear Regression (MLR) yang dioptimalkan dengan Particle Swarm Optimization (PSO) untuk memprediksi produksi padi di Pulau Madura. Data yang digunakan meliputi variabel terikat (produksi padi) dan variabel bebas seperti luas tanam, luas panen, dan produktivitas. Mean Absolute Percentage Error (MAPE) digunakan dalam menghiung kesalahan error model. Hasil pengujian menunjukkan bahwa pembagian data 50% untuk pelatihan dan 50% untuk pengujian menghasilkan hasil terbaik dengan MAPE sebesar 7,04%, yang lebih rendah dibandingkan dengan rasio data lainnya.
Peramalan Produksi Padi Menggunakan Extreme Learning Machine (ELM) Dengan Deteksi Outlier Wanda, Isnaini Septyana; Mufarroha, Fifin Ayu; Jauhari, Achmad; Anamisa, Devie Rosa
JUSIFOR : Jurnal Sistem Informasi dan Informatika Vol 4 No 1 (2025): JUSIFOR - Juni 2025
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/jusifor.v4i1.5858

Abstract

Peramalan produksi padi yang akurat sangat penting dalam mendukung perencanaan pertanian yang efisien. Penelitian ini mengusulkan kombinasi metode Extreme Learning Machine (ELM) dengan teknik deteksi outlier untuk meningkatkan akurasi peramalan produksi padi. Deteksi outlier digunakan untuk mengidentifikasi dan mengatasi anomali dalam data historis dari Dinas Pertanian Kabupaten Bangkalan (1997-2022). Setelah proses deteksi outlier, data digunakan untuk melatih model ELM guna memprediksi produksi berdasarkan luas panen. Hasil penelitian menunjukkan bahwa pendekatan ini secara signifikan meningkatkan akurasi dibandingkan dengan model ELM tanpa deteksi outlier.
Extreme Learning Machine Untuk Memprediksi Curah Hujan Dalam Penentuan Jadwal Tanam Padi Pratiwi, Indah; Bachtiar, Dimas; Jauhari, Achmad; Yusuf, Muhammad; Mufarroha, Fifin Ayu; Anamisa, Devie Rosa
JUSIFOR : Jurnal Sistem Informasi dan Informatika Vol 4 No 1 (2025): JUSIFOR - Juni 2025
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/jusifor.v4i1.5859

Abstract

Perubahan iklim dan pola curah hujan yang tidak menentu menjadi tantangan serius bagi pertanian, terutama dalam menentukan waktu yang tepat untuk tanam padi. Penelitian ini bertujuan untuk memodelkan curah hujan sebagai penentu jadwal tanam padi di Kabupaten Bangkalan, Madura. Pulau Madura merupakan pulau terbesar di Jawa Timur dengan total lahan kering seluas 77.999,64 ha di Kabupaten Bangkalan. Pertanian lahan kering sangat bergantung pada curah hujan sehingga memerlukan perencanaan tanam yang tepat untuk efisiensi penggunaan air. Metode Extreme Learning Machine (ELM) dipilih dalam penelitian ini karena dipilih karena kecepatan pelatihan yang tinggi dan kemampuan generalisasi yang baik. Data yang diolah terdiri dari 1.643 data curah hujan harian yang diperoleh dari BMKG Stasiun Meteorologi Perak I Surabaya, dengan rentang waktu dari Januari 2020 hingga Juni 2024. Dari hasil pengolahan data, diperoleh pengujian terbaik dengan rasio data latih dengan data uji sebesar 90:10, menggunakan satu hidden neuron, yang menghasilkan MAPE sebesar 2.489, MAE sebesar 0.0448, dan MSE sebesar 0.0086. Berdasarkan kriteria jadwal tanam padi, dapat disimpulkan bahwa jadwal tanam padi bisa dimulai pada bulan Oktober, karena curah hujan mencapai 150 mm dan mencapai lebih dari 100 mm pada tiga bulan setelahnya di tiap bulannya.
Prediksi Panen Padi Di Madura Dengan Triple Exponential Smoothing (TES) dan Algoritma Genetika Violina, Dina; Nuraini, Dinah; Anamisa, Devie Rosa; Khotimah, Bain Khusnul; Jauhari, Achmad; Mufarroha, Fifin Ayu
JUSIFOR : Jurnal Sistem Informasi dan Informatika Vol 4 No 1 (2025): JUSIFOR - Juni 2025
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/jusifor.v4i1.5860

Abstract

Dikenalnya Indonesia sebagai negara agraris karena sebagian besar wilayahnya digunakan untuk budidaya pertanian. Dengan keberhasilan panen padi sebesar 34.6 juta ton, Indonesia mampu menduduki peringkat keempat dunia untuk produksi padi Perannya sebagai sumber utama karbohidrat menjadikan padi banyak dipilih sebagai makanan sehari-hari di dunia, termasuk Indonesia. Khususnya di Madura, seiring bertambahnya jumlah pendudduk setiap tahunnya, kebutuhan pangan ikut bertambah. DI sisi lain, produksi padi yang terjadi di Madura setiap tahunnya mengalami fluktuasi. Oleh karena itu, untuk mengatasi permasalahan tersebut pada penelitian ini memanfaatkan teknologi sebagai upaya membantu petani dan pemerintah dalam menangani permasalahan agar tidak terjadi kekurangan. Penelitian ini dirancang dengan menerapkan metode Triple Exponential Smoothing (TES) yang memiliki tiga parameter (alpha, betha, dan gamma). Pemilihan parameter sangat berpengaruh pada  hasil akurasi, sehingga dalam penelitian ini ditambahkan Algoritma Genetika sebagai metode optimasi yang telah terbukti memberikan hasil yang baik pada beberapa penelitian. Algoritma Genetika pada penelitian ini digunakan untuk mengoptimalkan tiga parameter Triple Exponential Smoothing yang diperoleh hasil MAPE 9.74% dengan rasio data pelatihan 90% dan data pengujian 10%.
Co-Authors Achmad Jauhari Achmad Jauhari, Achmad Ahmad Try Bayu Al Haq Akhmad Tajuddin Tholaby Andharini Dwi Cahyani Ari Kusumaningsih Bachtiar, Dimas Bain Khusnul Khotimah Bain Khusnul Khotimah Dafid, Ach Devie Rosa Anamisa Doni Abdul Fatah Firli Irhamni Fitrianto, Hambali Ifandia, Alfian Mahendra Ika Oktavia Suzanti Indah Agustien Sirajuddin Indah Pratiwi Khusnul Fatimah Koeshardianto, Meidya Linawati Linawati Moh. Yusril Ihza Maulana Mohammad Yanuar Hariyawan Muhammad Amfahtori Wijarnoko Muhammad Yusuf Mulaab Mulaab Nuraini, Dinah Okie Maria Amul Husnah Rachmad Masbadi Hatullah Nurnaryo Rika Yunitarini Septiyanto, Triyas Siradjuddin, Indah Agustien Sukri, Hanifudin Violina, Dina Wahyu Indra Kustina Wanda, Isnaini Septyana Yeni Kustiyahningsih Yudha Dwi Putra Negara, Yudha