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All Journal Jurnal Simantec International Journal of Advances in Intelligent Informatics Jurnal Pendidikan: Teori, Penelitian, dan Pengembangan JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi Jurnal ULTIMATICS Jurnal ULTIMA Computing Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Brilliance: Research of Artificial Intelligence NUSANTARA: Jurnal Pengabdian Kepada Masyarakat Digital Transformation Technology (Digitech) Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer AL-UMRON: Jurnal Pengabdian kepada Masyarakat Jurnal Pengabdian Masyarakat dan Riset Pendidikan Almantiq
Irsyada, Rahmat
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Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Penerapan Metode Moving Average Pada Sistem Informasi Prediksi Angka Kemiskinan Didik Abdul Mukmin; Rahmat Irsyada; Hastie Audytra Audytra
Multidisciplinary Applications of Quantum Information Science (Al-Mantiq) Vol. 1 No. 1 (2021): Multidisciplinary Applications of Quantum Information Science (Al-Mantiq)
Publisher : Al-Mantiq

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/almantiq.v1i1.330

Abstract

Sebagian permasalahan kehidupan manusia sejak dahulu kala adalah kemiskinan. Berdasarkan data Badan Pusat Statistik, kenaikan persentase penduduk miskin pada September 2013 disebabkan oleh kenaikan harga barang kebutuhan pokok sebagai akibat dari kenaikan harga bahan bakar minyak pada bulan Juni 2013. Menyikapi kondisi tersebut, diperlukan suatu cara untuk mengetahui seberapa besar penurunan atau kenaikan jumlah penduduk miskin. Diperlukan proses peramalan (forecasting) berdasar pada data jumlah penduduk miskin tahun sebelumnya. Metode peramalan yang digunakan dalam penelitian ini adalah moving average atau rata-rata bergerak, dikarenakan metode ini banyak digunakan untuk menentukan trend dari suatu deret waktu.Tujuan utama dari penggunaan metode ini adalah untuk menghilangkan atau mengurangi acakan (randomness) dalam deret waktu. Peramalan kemiskinan dilakukan berdasarkan data kemiskinan se-Indonesia yang diambil dari Badan Pusat Statistik (BPS). Dari data tahun 2012-2019, perhitungan dilakukan dengan periode (3) dan tahun akhir peramalan 2024. Hasil dari perhitungan tersebut menjelaskan bahwa pada 2 tahun terakhir yaitu tahun 2023 bulan maret terjadi penurunan angka kemiskinan sebanyak 0,0029%, tahun 2023 bulan september naik 0,082%, tahun 2024 bulan maret turun 0,138%, tahun 2024 bulan september naik 0,093%.
Sistem Pendukung Keputusan Pengangkatan Pegawai Tetap Pada Lembaga Pendidikan Ma’arif Nu Cabang Bojonegoro Menggunakan Metode Topsis Rubaiyah, Zumrotul; Irsyada, Rahmat
Multidisciplinary Applications of Quantum Information Science (Al-Mantiq) Vol. 4 No. 2 (2024): Multidisciplinary Applications of Quantum Information Science (Al-Mantiq)
Publisher : Al-Mantiq

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/almantiq.v4i2.3288

Abstract

Pegawai menjadi elemen kunci dalam kesuksesan organisasi, termasuk Lembaga Pendidikan Ma'arif NU Cabang Bojonegoro. Pengangkatan pegawai tetap menjadi tahap penting dalam memastikan kualitas sumber daya manusia yang mendukung visi dan misi lembaga. Penelitian ini mengembangkan Sistem Pendukung Keputusan (SPK) berbasis komputer menggunakan metode Technique Order Preference by Similarity To Ideal Solution (TOPSIS) untuk mempercepat dan meningkatkan akurasi pengangkatan pegawai tetap di Lembaga Pendidikan Ma'arif NU Cabang Bojonegoro. Proses manual yang ada telah mengakibatkan ketidakpastian data dan kebutuhan akan sistem yang lebih efisien. Dengan SPK berbasis web, seleksi pegawai dapat dipercepat, meningkatkan produktivitas sesuai kriteria yang ditentukan. Hasil penelitian menunjukkan keberhasilan implementasi TOPSIS dalam pengujian blackbox, dengan sistem yang layak dan sesuai dengan fungsi yang diharapkan. Dengan persentase 94% dalam pengujian kelayakan, sistem ini diharapkan dapat meningkatkan efisiensi dan efektivitas pengangkatan pegawai tetap di lembaga tersebut.
Improving Multiclass Rainfall Prediction with Multilayer Perceptron and SMOTE: Addressing Class Imbalance Challenges Cahyani, Nita; Putri, Wardiana Adinda; Irsyada, Rahmat
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.5203

Abstract

Rainfall is a key climate element that affects weather patterns and human activities, especially in agriculture and daily life. Therefore, accurately classifying rainfall is crucial for predicting future rainfall amounts. This study uses the Multilayer Perceptron (MLP) classification method, a neural network algorithm, to classify rainfall. The dataset, sourced from the BMKG website, has a class imbalance, requiring using the SMOTE (Synthetic Minority Over-sampling Technique) technique. The research compares the performance of MLP with and without SMOTE. The results show that the best model was achieved with SMOTE. MLP without SMOTE achieved an accuracy of 75%, sensitivity of 40.34%, specificity of 86.15%, and an AUC of 63.25%. In comparison, MLP with SMOTE achieved an accuracy of 71.27%, sensitivity of 71.14%, specificity of 90.30%, and an AUC of 80.72%. Although accuracy decreased, the overall evaluation, particularly the AUC, improved significantly. Therefore, the SMOTE technique effectively addresses the class imbalance issue in rainfall classification.
Innovation of an Expert System for Diagnosing Allergic Diseases in Children using the Web-based Certainty Factor Method Irsyada, Rahmat; Cahyani, Nita; Badriyah, Lailatul
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.5204

Abstract

In this modern era, the development of computer technology has increased so rapidly. Currently the computer is a tool in helping to overcome all the problems encountered by humans, including in the field of health. With the existence of technology, of course, it will greatly facilitate the community to get health services and consultations. One of the technological developments is an expert system. An expert system is a branch of artificial intelligence (Artificial Intelligence), which is an application designed to use a computer that tries to imitate the reasoning process of an expert or expert in solving specific problems and making decisions or conclusions because to solve a problem and save it. in the knowledge base for processing. This expert system was created to assist experts in deciding diseases based on existing symptoms. The Certainty Factor method is a theory that can be used to solve uncertainty problems. Certainty Factor (CF) is a value to measure expert confidence. Certainty Factor was introduced by Shortliffe Buchanan in making the MYCIN expert system to show the amount of trust. This method can work well when there are problems that start from gathering and then gathering information and then being able to find conclusions that can be drawn from that information. The Certainty Factor method will be applied to accurately determine allergic health in children. If this method is applied, it can minimize the presence of allergic diseases suffered by dangerous children. And when you have an allergy, it can be treated immediately.
Family Hope Program Recipient Determination System Using The Naive Bayes Method Irsyada, Rahmat; Cahyani, Nita; Mu’afa , M Rif’an Fawajul; Perdana , Chepy; Febriyanto, Erick
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6362

Abstract

Poverty is still a problem that Indonesian people continue to face. To achieve prosperity and social justice for all Indonesian citizens, poverty can be considered a situation where a person does not have the ability to fulfill their basic needs, such as food, shelter, clothing, has a low income, has limited access to education, and has work skills. which is inadequate. The government, as a policy maker, has made various efforts to reduce poverty, one of which is through the Family Hope Program (PKH). However, in its implementation, the distribution of PKH assistance still faces problems in terms of targeting accuracy. To overcome this problem, a system is needed that can provide recommendations about who is worthy of receiving PKH assistance. One approach that can be used is a decision support system (DSS) using the Naïve Bayes method. Naïve Bayes is an algorithm used for text classification and is a Machine Learning method that focuses on calculating probability and statistics to predict future probabilities based on past experience. With the help of SPK, this system is able to provide recommendations about who should receive assistance. PKH is based on criteria such as school children, toddlers, pregnant women, the elderly and people with disabilities. Test results using the Naïve Bayes method with Confusion Matrix calculations show an accuracy level of 75%. Next, a comparison was carried out with testing using Cross Validation, which showed an increase in accuracy compared to previous testing without using 10-fold Cross Validation.
Performance Comparison of SelectKBest and Permutation Importance in Feature Selection for Diabetes Prediction Cahyani, Nita; Irsyada, Rahmat
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6507

Abstract

This study evaluates the effectiveness of two feature selection methods, namely the statistics-based SelectKBest and the model-based Permutation Importance, in improving the performance of classification algorithms for diabetes prediction. A dataset consisting of 17 clinical and demographic features was used to train 11 machine learning algorithms with two subsets of selected features. Performance evaluation used accuracy, precision, recall, F1-Score, ROC AUC, and training time. Based on the results, the SelectKBest method was able to improve the performance of Random Forest with an accuracy of 82.7%, a precision of 0.8, a recall of 0.5, and an F1-Score of 0.615. Meanwhile, the Permutation Importance method showed more consistent performance, with six models including Random Forest, K-Nearest Neighbors, and Quadratic Discriminant Analysis (QDA) achieving an accuracy of up to 86.2%. QDA stood out with the highest ROC AUC of 0.887, indicating better class detection capabilities. These findings underscore the superiority of Permutation Importance in selecting relevant and varied features, including demographic factors, thereby improving overall prediction accuracy. In practice, Random Forest with SelectKBest is recommended for applications requiring fast and interpretable models, while QDA and Gradient Boosting with Permutation Importance are recommended for those requiring high accuracy and sensitivity. This study strengthens the foundation for developing more accurate and applicable diabetes prediction models across various contexts.
Profit Prediction for Skincare Resellers Using the Exponential Smoothing Method Cahyani, Nita; Irsyada, Rahmat; Firman, Azharil; Inayaturohmat, Fatuh; Pramesti, Retta Farah
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6585

Abstract

This research elucidates the application of the exponential smoothing method in forecasting profit figures for Lutfia MS Glow Skincare. This method was chosen due to its capability to adapt data using the alpha value, along with continual refinement based on exponentially smoothed historical averages. With an explanatory purpose, the study collected profit data from 2020 to 2022 at Lutfia MS Glow Skincare. The single exponential smoothing technique was employed to develop a profit prediction system, enabling the identification of sales trends and evaluation through metrics like Mean Absolute Error (MAE) and Mean Squared Error (MSE). The approach offers simplicity in implementation while providing relatively accurate results, especially for short-term forecasting. This makes it particularly useful in retail and skincare business contexts, where sales figures can be volatile due to seasonal demands or market fluctuations. By utilizing exponential smoothing, the research helps reduce forecasting errors and provides actionable insights for business planning. The result of the analysis showed a reasonably low error margin with a Mean Absolute Percentage Error (MAPE) of 3.65%, indicating high prediction accuracy. The research outcomes furnish skincare resellers and decision-makers with practical guidance in planning inventory, managing supply chains, and executing marketing strategies, ultimately supporting better data-driven decisions in a competitive industry.
Pengembangan Kipas Pemadam Api Otomatis Berbasis Iot Izudin, Asfa Reza; Irsyada, Rahmat; Wahyudhi , Sunu
Multidisciplinary Applications of Quantum Information Science (Al-Mantiq) Vol. 4 No. 1 (2024): Multidisciplinary Applications of Quantum Information Science (Al-Mantiq)
Publisher : Al-Mantiq

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Tujuan dari kipas pemadam api berbasis Internet of Things (IoT) adalah untuk meningkatkan efisiensi dan efektivitaspenanganan kebakaran kecil di rumah dan tempat bisnis. Sistem ini memiliki sensor asap dan suhu yang dapat mendeteksikebakaran secara real-time dan kemudian mengaktifkan kipas pemadam api otomatis untuk meredam api pada tahap awal.Aplikasi mobile memungkinkan pengguna memantau dan mengontrol sistem dari jarak jauh, memungkinkan pemantauan danrespons cepat. Hasil pengujian menunjukkan bahwa sistem dapat merespons kebakaran dalam hitungan detik dan berhasilmencegah penyebaran api dan memberikan notifikasi instan kepada pengguna, meningkatkan keamanan dan mengurangi risikokerugian.
Integrated Community-Based Disaster Response Information System: A Case Study of the Subang Regency BPBD Iqbal, Mohammad; Febriyanto, Erick; Irsyada, Rahmat
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6985

Abstract

Subang Regency is an area highly prone to natural disasters such as floods, landslides, and strong winds. In emergency situations, the absence of an efficient and integrated reporting system greatly hinders Badan Penanggulangan Bencana Daerah (BPBD) in carrying out rapid responses, evacuations, and aid distribution. If not addressed promptly, public safety and the effectiveness of disaster management will remain at risk. Therefore, a disaster mitigation application system is needed that allows the community to quickly report disasters through photos, videos, and descriptions directly integrated with the BPBD dashboard. This application is equipped with multi-channel notifications via WhatsApp, SMS Gateway, and alarms, as well as an AI-based heatmap analytics system to predict potential disasters using historical data and weather information from BMKG. In addition, BPBD administrators can verify disaster reports by checking personal biodata linked to the reporter’s account. The system development method applied is Agile Development, which includes observation, planning, design, development, testing, and deployment, enabling intensive collaboration and rapid system iterations based on field feedback. With this system, BPBD Subang is expected to be more responsive and resilient in facing disasters.
Analisis Faktor Makroekonomi yang Mempengaruhi Indeks Harga Saham Gabungan Menggunakan Algoritma Analisis Jalur Cahyani, Nita; Irsyada, Rahmat; Alfiyatul, Siti Nur
Digital Transformation Technology Vol. 4 No. 2 (2024): Periode September 2024
Publisher : Information Technology and Science(ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/digitech.v4i2.5207

Abstract

Perkembangan ekonomi yang baik pada suatu negara merupakan suatu indikator yang digunakan oleh para pelaku usaha untuk berinvestasi. Sebelum berinvestasi dalam sebuah saham, investor harus memperhatikan pergerakan harga saham. Indeks Harga Saham dipengaruhi oleh beberapa faktor makroekonomi, antara lain inflasi dan suku bunga BI. Upaya yang dilakukan pemerintah dalam mengatasi tingginya inflasi salah satunya adalah dengan mengurangi jumlah uang yang beredar. Selain inflasi dan suku bunga, nilai tukar uang juga dapat mempengaruhi indeks harga saham.Penelitian ini bertujuan untuk mengetahui faktor apa saja yang mempengaruhi indeks harga saham gabungan. Metode analisis yang digunakan dalam penelitian ini adalah metode analisis jalur. Hasil penelitian menyatakan bahwa jumlah uang beredar, nilai tukar uang dan suku bunga BI secara langsung secara sigifikan mempengaruhi indeks harga saham gabungan, sedangkan inflasi secara langsung secara signifikan tidak mempengaruhi indaks harga saham gabungan. Jumlah uang beredar, inflasi dan nilai tukar uang berpengaruh signifikan terhadap indeks harga saham gabungan melalui suku bunga BI.
Co-Authors Agus Sulistiawan Ahmad Dardiri Aji Prasetya Wibawa Akhmad Arif Setiawan Alfiyatul, Siti Nur Anggi, Diva Dhaniyar Dhaniyar Diantika, Devi Eka Didik Abdul Mukmin Doni Abdul Fatah Erick Febriyanto Firman, Azharil Hastie Audytra Hastie Audytra Audytra Hastie Audytra Audytra Hilmi Alriad Inayaturohmat, Fatuh Indah Listyorini Izudin, Asfa Reza Joko Riyadi Juwita Annisa Fauzi Kartini, Alif Yuanita Lailatul Badriyah Leonel Hernandez M.Pd S.T. S.Pd. I Gde Wawan Sudatha . Mahmuda, Rahmawati Mohammad Iqbal Muhdlor Auhal Haq Mu’afa , M Rif’an Fawajul Naila Afina Rohmah Nita Cahyani Nurul Hafidhin Perdana , Chepy Pramesti, Retta Farah Prima Angga Hadi Saputra Putri Liana Putri, Wardiana Adinda Roihatur Rohmah Roikhatul Jannah Rubaiyah, Zumrotul Sanjaya, Ucta Pradema Seno Isbiyantoro Sunu Wahyudhi Syaifuddin Syaifuddin Teguh Pribadi Ulfa Ulfa Umi Kholifah Yundari, Yundari