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Wanayumini
Universitas Asahan
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RANCANG BANGUN SISTEM REKOMENDASI PEMILIHAN SAHAM LOW-RISK BERBASIS FUZZY TSUKAMOTO PADA PASAR MODAL INDONESIA Puan Maharani; Wanayumini Wanayumini
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.4814

Abstract

Abstract: The Indonesian capital market demonstrated rapid growth throughout 2025. According to a press release issued by the Indonesia Stock Exchange (IDX) on February 10, 2025, a significant surge in the number of national capital market investors was recorded. By early January 2025, the number of Single Investor Identifications (SIDs) had exceeded 15 million, marking the highest achievement in the development of financial inclusion in Indonesia. The stock data used was limited to companies listed on the Indonesia Stock Exchange and consistently included in the LQ45 index. The objective of this study was to design a low-risk stock selection model that uses the Tsukamoto fuzzy method to manage various criteria based on market data. The result sum_alpha = 0.333 was obtained from the highest rule evaluation value (alpha), and to obtain sum_alpha_z = 8.88, the highest rule evaluation value (alpha_z) was obtained to obtain a z_final value of 26.66 (sum_alpha_z value / sum_alpha value). This stock recommendation system can systematically select low-risk stock recommendations. Keywords: Design, Recommendation System, Low-Risk Stock Selection, Fuzzy Tsukamoto, Indonesian Capital Market Abstrak: Pasar modal Indonesia menunjukkan pertumbuhan yang pesat sepanjang tahun 2025. Berdasarkan siaran pers yang diterbitkan oleh PT Bursa Efek Indonesia (BEI) pada 10 Februari 2025, tercatat lonjakan signifikan dalam jumlah investor pasar modal nasional. Hingga awal Januari 2025, jumlah Single Investor Identification (SID) telah melampaui 15 juta, yang menandai capaian paling tinggi dalam perkembangan inklusi keuangan di Indonesia. Data saham yang digunakan terbatas pada perusahaan-perusahaan yang terdaftar di bursa efek Indonesia dan secara konsisten masuk dalam indeks LQ45. Tujuan dalam penelitian ini adalah merancang sebuah model seleksi saham berisiko rendah yang menggunakan metode fuzzy Tsukamoto untuk mengelola berbagai kriteria berbasis data pasar. hasil sum_alpha=0.333 didapat dari nilai tertinggi rule evaluation (alpha) dan untuk mendapatkan nilai sum_alpha_z=8.88 didapat dari nilai tertinggi rule evaluation (alpha_z) untuk mendapat nilai z_final=26.66 (nilai sum_alpha_z/ nilai sum_alpha) hasil. Sistem rekomendasi saham ini dapat melakukan proses seleksi secara sistem tentang rekomendasi saham yang rendah resiko. Kata Kunci: Rancang Bangun, Sistem Rekomendasi, Pemilihan Saham Low-Risk, Fuzzy Tsukamoto, Pasar Modal Indonesia
KLASIFIKASI TIPE KONSUMEN BERDASARKAN RIWAYAT TRANSAKSI MENGGUNAKAN METODE K-NEAREST NEIGHBOR (KNN) Al Izzati Karimah; Wanayumini
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 2 (2026): April 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i2.6262

Abstract

Abstract: Advances in digital technology have generated consumer transaction history data that can be used to analyze consumer behavior and types. However, this data has not yet been optimally utilized in the consumer segmentation process. This study aims to develop a consumer type classification system based on transaction history using the K-Nearest Neighbor (KNN) method. The dataset used was sourced from Kaggle and included variables such as product type, brand, category, quantity, price, and payment method. The research stages included data collection, preprocessing, model training and testing, and evaluation of classification results. The system was built using PHP and MySQL. The KNN method was used to group consumers into Regular, Premium, and Optimal categories. Testing results using 100 data points and setting K=5 showed an accuracy of 65%; the study found that the K-Nearest Neighbor (KNN) method successfully classified the data. Keywords: Classification, Consumer Type, Transaction History, Data Mining, K-Nearest Neighbor (KNN). Abstrak: Perkembangan teknologi digital menghasilkan data riwayat transaksi konsumen yang dapat dimanfaatkan untuk menganalisis perilaku dan tipe konsumen. Namun, data tersebut belum dimanfaatkan secara optimal dalam proses pengelompokan konsumen. Penelitian ini bertujuan membangun sistem klasifikasi tipe konsumen berdasarkan riwayat transaksi menggunakan metode K-Nearest Neighbor (KNN). Dataset yang digunakan berasal dari Kaggle dengan variabel jenis produk, merek, kategori, kuantitas, harga, dan metode pembayaran. Tahapan penelitian meliputi pengumpulan data, prapemrosesan, pelatihan dan pengujian model, serta evaluasi hasil klasifikasi. Sistem dibangun menggunakan PHP dan MySQL. Metode KNN digunakan untuk mengelompokkan konsumen ke dalam kategori Reguler, Premium, dan Optimal. Hasil pengujian menggunakan 100 data dan mencari K=5 menunjukkan hasil akurasi sebanyak 65%, dari penelitian yang dilakuakn Metode K-Nearest Neighbor (KNN) berhasil mengklasifikasikan dengan baik. Kata Kunci: Klasifikasi, Tipe Konsumen, Riwayat Transaksi, Data Mining, K-Nearest Neighbor (KNN).
IMPLEMENTASI REGRESI LINIER BERGANDA UNTUK PREDIKSI PENJUALAN PRODUK HOME BRAND PADA OUTLET FARMASI Miftahul Jannah; Wanayumini
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 2 (2026): April 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i2.6395

Abstract

Abstract: Sales prediction is important for pharmacy retail businesses to support stock planning and reduce dependence on subjective decision-making. This study aims to implement Multiple Linear Regression to predict the sales of home brand products at a pharmacy outlet and integrate the prediction model into a web-based system. The data used in this study were daily sales transaction data from Sri Manja Pharmacy Outlet from May 16, 2024 to April 16, 2026. The initial dataset consisted of 124,992 transaction records, which were filtered, cleaned, aggregated into daily sales data, and transformed into predictive variables, including time-based variables, historical sales patterns, and discount variables. The model was developed separately for each product using an 80:20 time-based data split and evaluated using MAE, RMSE, MAPE, and R². The results show that the model can predict sales with varying performance across products. One tested product achieved a MAPE of 12.21% and an R² of 0.9068, indicating relatively good predictive performance. The model was successfully implemented into a web-based system that supports sales prediction, visualization, and report generation. Therefore, the proposed system can assist pharmacy outlets in making more data-driven sales and stock planning decisions. Keywords: Home Brand; Multiple Linear Regression; Pharmacy Outlet; Sales Prediction; Web-Based System.   Abstrak: Prediksi penjualan memiliki peran penting dalam bisnis ritel farmasi untuk mendukung perencanaan stok dan mengurangi ketergantungan terhadap pengambilan keputusan secara subjektif. Penelitian ini bertujuan untuk mengimplementasikan Regresi Linear Berganda dalam memprediksi penjualan produk home brand pada satu outlet farmasi serta mengintegrasikan model prediksi ke dalam sistem berbasis web. Data yang digunakan merupakan data transaksi penjualan harian Outlet Farmasi Sri Manja periode 16 Mei 2024 sampai 16 April 2026. Dataset awal terdiri dari 124.992 baris transaksi, kemudian dilakukan seleksi, pembersihan, agregasi menjadi data penjualan harian, dan pembentukan variabel prediksi yang meliputi variabel waktu, pola historis penjualan, dan variabel diskon. Model dibangun secara terpisah untuk setiap produk menggunakan pembagian data berbasis waktu dengan rasio 80:20 serta dievaluasi menggunakan MAE, RMSE, MAPE, dan R². Hasil penelitian menunjukkan bahwa model mampu memprediksi penjualan dengan performa yang berbeda pada setiap produk. Salah satu produk yang diuji memperoleh nilai MAPE sebesar 12,21% dan R² sebesar 0,9068, yang menunjukkan performa prediksi cukup baik. Model berhasil diimplementasikan ke dalam sistem berbasis web yang mendukung prediksi, visualisasi, dan pembuatan laporan penjualan. Dengan demikian, sistem yang diusulkan dapat membantu outlet farmasi dalam pengambilan keputusan penjualan dan perencanaan stok berbasis data. Kata Kunci: Home Brand; Outlet Farmasi; Prediksi Penjualan; Regresi Linier Berganda; Sistem Berbasis Web.
JARINGAN SYARAF TIRUAN DENGAN ALGORITMA HEBB RULE UNTUK DIAGNOSA PENYAKIT PARU-PARU Irfan Darmansyah; Wanayumini
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 3 (2026): June 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i3.6472

Abstract

Lung diseases are a serious health issue that requires prompt and accurate treatment. However, in practice, medical professionals often face challenges such as high patient volumes, limited examination time, and the similarity of symptoms across different types of lung diseases, which make it difficult to consistently establish an initial diagnosis. This study aims to design and develop a medical decision support system to diagnose lung diseases using an Artificial Neural Network (ANN) with the Hebb Rule algorithm. The types of diseases focused on in this study include Asthma, Bronchitis, COPD, and Pulmonary TB. The research methodology utilized 100 patient medical records from H. OK Arya Zulkarnain General Hospital as training data, consisting of 39 clinical symptom variables. The system was developed using the PHP programming language and a MySQL database. The Hebb Rule algorithm was applied to perform network weight learning so that the system could recognize patterns of relationships between symptoms and disease types based on historical data. The results of the study show that the Hebb Rule algorithm was successfully implemented into a web-based system capable of generating diagnostic decisions based on the highest activation values in the output neurons. This system can process patient symptom data quickly and provide prediction results consistent with the training data patterns. This study concludes that the use of ANNs with the Hebb Rule method is effective as a tool for early detection and decision support for medical personnel to improve the efficiency of healthcare services in hospitals.
ANALISIS JARINGAN SYARAF TIRUAN UNTUK KLASIFIKASI KASUS KEKERASAN TERHADAP PEREMPUAN DEWASA MENGGUNAKAN ALGORITMA LEARNING VECTOR QUANTIZATION (LVQ) Selfina Agustin; Wanayumini
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 3 (2026): June 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i3.6642

Abstract

Violence against adult women is a social problem that still frequently occurs in Indonesia and shows an increase every year. Available data on violence cases are still in the form of raw data, making it difficult to use to determine the level of vulnerability in each region. Therefore, a method is needed that can process this data into more meaningful information through a classification process. This study aims to classify the level of violence against adult women in Indonesia using the Learning Vector Quantization (LVQ) algorithm. This data uses seven attributes, namely physical violence, psychological violence, sexual violence, exploitation, human trafficking, neglect, and others. The classification process is carried out into three classes, namely high, medium, and low. The test results show that the first data for the Aceh Province region is classified into class 1 (high) with a distance value of 111.7157645406. The second data for the North Sumatra Province region is also classified into class 1 (high) with a value of 114.41286640237. Meanwhile, the 3rd data for the West Sumatra Province region is classified into class 3 (low) with a value of 104.51753457. The results of the study indicate that the Learning Vector Quantization (LVQ) algorithm is able to group data on cases of violence against adult women based on their level of vulnerability so that it can be used as supporting information in decision-making and policy formulation for handling cases of violence against women.
PENERAPAN JARINGAN SYARAF TIRUAN UNTUK PREDIKSI JUMLAH PENUMPANG KERETA API MENGGUNAKAN ALGORITMA BACKPROPAGATION Zulfa AR Rahman; Wanayumini
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 3 (2026): June 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i3.6645

Abstract

The number of rail passengers on the Kisaran–Medan route varies over time due to a number of variables, including local economic situations, national holidays, peak travel seasons, and current transportation regulations. The administration of Kisaran Station finds it challenging to manage train fleets, organize travel timetables, and enhance service quality due to this unpredictability. Using historical data from 2019 to 2025, this study intends to apply an Artificial Neural Network (ANN) with the Backpropagation technique to forecast the number of train passengers at Kisaran Station. With a learning rate of 0.25 and a sigmoid binary activation function, the system was constructed using a 7-5-1 network architecture with seven input neurons (yearly data from 2019–2025), five hidden layer neurons, and one output neuron. The data was split into 70% training data (January–July) and 30% testing data (August–December) after being adjusted to the interval 0.1–0.9 using the Min-Max technique. At epoch 822, the training process reached a convergence. For most months, including March (predicted: 647,271; actual: 642,870 passengers) and April (predicted: 999,728; actual: 996,320 passengers), the model was able to produce predictions that were close to actual values, according to testing results. However, there were differences in some months with seasonal spikes. It is anticipated that PT Kereta Api Indonesia and the regional government would use the created prediction system as a decision-support tool to plan transportation services and enhance the general quality of railway services.
PENERAPAN JARINGAN SYARAF TIRUAN UNTUK INDEKS PEMBANGUNAN MANUSIA MENGGUNAKAN ALGORITMA PERCEPTRON Emi Dea; Wanayumini
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 3 (2026): June 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i3.6646

Abstract

This study aims to analyze and predict the level of the Human Development Index (HDI) using the Artificial Neural Network (ANN) method with the Perceptron algorithm. The problem underlying this research is the large amount of data and the numerous variables affecting HDI, making manual analysis less effective and time-consuming. The data used in this study include Life Expectancy at Birth, Expected Years of Schooling, Mean Years of Schooling, and Adjusted Expenditure per Capita. The research was conducted using Human Development Index data obtained from the Family Information System (SIGAThe dataset was divided into training data and testing data to evaluate the model's ability to predict HDI levels into two categories, namely high and low. The system was developed using the PHP programming language and MySQL database and was designed using Unified Modeling Language (UML).The results of this study indicate that the Artificial Neural Network method with the Perceptron algorithm is capable of predicting Human Development Index levels effectively based on the available data. The Perceptron model was able to recognize the relationship patterns among Life Expectancy at Birth, Expected Years of Schooling, Mean Years of Schooling, and Adjusted Expenditure per Capita variables with HDI levels. The testing results produced a final Mean Squared Error (MSE) value of 0.141227 with an accuracy rate of 80.00% after the training process stopped at the 115th epoch with a learning rate of 0.1. The developed system can assist in the analysis and prediction of human development levels and can be used as a decision-support tool in human development planning.  
Analisis Sentimen Opini Publik Terhadap Penundaan Pengangkatan CPNS 2025 Menggunakan Machine Learning Andreas Rezeki Zai; Rika Rosnelly; Wanayumini Wanayumini
Jurnal Minfo Polgan Vol. 15 No. 2 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i2.16474

Abstract

Penjadwalan ulang penunjukan Calon Pegawai Negeri Sipil pada tahun 2025 telah memicu berbagai pandangan publik dan menjadi isu yang banyak diperdebatkan di media sosial X.  Penelitian ini berusaha untuk menyelidiki sentimen publik mengenai penjadwalan ulang penunjukan Calon Pegawai Negeri Sipil pada tahun 2025 dengan menggunakan metodologi Machine Learning dan menganalisis dampak optimasi Algoritma Genetik terhadap akurasi klasifikasi.  Komentar sebanyak 16.075 dikumpulkan dari media sosial X dalam rentang waktu 11 Maret 2025 hingga 1 Juni 2025.  Tahapan pra-pemrosesan mencakup pembersihan data, pengubahan huruf besar-kecil, normalisasi kata, tokenisasi, penghapusan kata-kata umum, stemming, penanganan negasi, dan penghapusan catatan duplikat.  Klasifikasi sentimen otomatis dicapai melalui strategi InSet Lexicon, yang menghasilkan tiga jenis sentimen: negatif, netral, dan positif.  Frekuensi Istilah Inverse Frekuensi Dokumen digunakan untuk ekstraksi fitur, yang kemudian diikuti dengan klasifikasi menggunakan Naive Bayes dan Support Vector Machine.  Algoritma Genetika diterapkan untuk pemilihan fitur dan penyetelan hiperparameter guna meningkatkan efektivitas model.  Analisis dilakukan melalui penggunaan akurasi, presisi, recall, F1-score, matriks kebingungan, cross-validation, Area Under Curve, dan Uji Wilcoxon Signed Rank.  Penelitian menunjukkan bahwa model Naive Bayes memperoleh akurasi sebesar 87,11 persen, sementara model yang ditingkatkan mencapai akurasi sebesar 87,61 persen.  Model Support Vector Machine mencapai akurasi sebesar 95,18 persen, sementara model yang telah disempurnakan memberikan kinerja tertinggi dengan akurasi 96,02 persen, F1-score 96,01 persen, dan Area Under Curve 99,14 persen.  Hasil dari pemeriksaan signifikansi statistik menunjukkan bahwa peningkatan kinerja setelah optimasi adalah signifikan.  Oleh karena itu, Algoritma Genetik telah membuktikan potensinya untuk meningkatkan hasil kategorisasi sentimen, dengan model Support Vector Machine yang dioptimalkan diakui sebagai model unggulan dalam analisis kami.
Co-Authors Ade Clinton Sitepu Ade Clinton Sitepu Adelina, Mimi Chintya Agung RM Alam Al Ayyub, Muhammad Azwar Al Izzati Karimah Albert Putra Nias Manao Alfitra, Andra Amanda, Windi Winona Ammar Yasir Nasution Andi Zulherry Andreas Rezeki Zai Annas Prasetio Annas Prasetio Ardana, Abdul Aziz Arjuna Ginting ayadi, B. Herawan H B. Herawan Hayadi Cici Cahyati Hasibuan Dame Lasmaria Simangunsong Darma, Ali Dedy Hartama Dedy Hartama Desi Irfan Desi Irfan Devy Pratiwi Dini Farhatun Doughlas Pardede Elisabeth S, Noprita Elsa Rahmadani Emi Dea Erica Rian Safitri Erlina Erlina Fajar Hardiansyah Gea, Muhammad Nasri Habib Satria Hanani Hutabarat, Jamina Harahap, Sarwedi Hartama, Dedy Hartono Hartono Hasibuan, Cici Cahyati Husin Sariangsah Ichsan Firmansyah Indra Mawanta Indra Swanto Ritonga Irfan Darmansyah Irfan Sudahri Damanik isnaini, fitri JAKA KUSUMA Juni Ismail Karina Andriani Khoirunsyah Dalimunthe Lili Tanti Lubis, Cindy Paramitha lvindra, Farhan A M yoggi saputra M. Ari Iskandar Maharani, Puan Margolang, Khairul Fadhli Masri Wahyuni Mhd Fauzan Yafi Mhd Zahir Az Zikri Miftahul Jannah Miftahul Jannah Muhammad Fachrurrozi Nasution Muhammad Nasri Gea Muhammad Sadikin Muhammad Sayid Amir Ali Lubis Muhammad Zarlis Mutiara S. Simanjuntak Nasir Fadillah Marpaung Nazifa Putri Novendra Adisaputra Sinaga NURLIANA NURLIANA Nurul Akmal Jodhy P.P.P.A.N.W. Fikrul Ilmi R.H. Zer Prasetya, Hardi Puan Maharani Putri, Nazifa Rahma, Intan Dwi Rahmat Rika Rosnelly Rika Rosnelly RIKA ROSNELLY Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly, Rika Roesnelly, Rika Rohima, Rohima Roslina Roslina, Roslina Roslina, Roslina Safitri, Erica Rian Sartika Mandasari Selase, Septinur Selfina Agustin Sihombing, Rotua Simangunsong, Dame Lasmaria Sri Ayu Rosiva Srg Sugeng Riyadi Sugeng Riyadi Sultan Nico Nur'Arsy Sumantri, Ekoliyono Wahyu Syahrizal Syahrizal T S Gunawan Tambunan, Fazli Nugraha Tammamah Lubis, Hartati Teddy Surya Gunawan Teddy Surya Gunawan Teddy Surya Gunawan Teddy Surya Gunawan Teddy Surya Gunawan Triana Puspa handayani Triwanda, Eri Vicky Rolanda Wardana, Revo Wulandari, Wulandari Yuni Franciska Tarigan Zakarias Situmorang Zer, P.P.P.A.N.W. Fikrul Ilmi R.H. Zulfa Ar Rahman