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All Journal TEKNIK INFORMATIKA Jurnal Simetris JURNAL DERIVAT: JURNAL MATEMATIKA DAN PENDIDIKAN MATEMATIKA Prosiding SNATIF NUMERICAL (Jurnal Matematika dan Pendidikan Matematika) Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi JURNAL MANAJEMEN (EDISI ELEKTRONIK) Shirkah: Journal of Economics and Business Simtek : Jurnal Sistem Informasi dan Teknik Komputer STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Jurnal Teknologi Informasi dan Multimedia Jurnal Informatika dan Rekayasa Elektronik Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] G-Tech : Jurnal Teknologi Terapan JUKI : Jurnal Komputer dan Informatika Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat TIN: TERAPAN INFORMATIKA NUSANTARA Infotech: Journal of Technology Information Riset Pendidikan Bahasa dan Sastra Indonesia (Repetisi) International Journal of Community Service Simpatik: Jurnal sistem Informasi dan Informatika Buletin Sistem Informasi dan Teknologi Islam Journal of Management and Digital Business Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Duta Abdimas: Jurnal Pengabdian Masyarakat Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Innovative: Journal Of Social Science Research Nusantara Journal of Computers and its Applications Jurnal INFOTEL SmartComp CSRID Jurnal Sosialita: Jurnal Kajian Sosial dan Pendidikan Journal of Information Technology RESWARA: Jurnal Riset Ilmu Teknik Jurnal Teknik Informatika dan Teknologi Informasi
Nurmalitasari Nurmalitasari
Universitas Duta Bangsa Surakarta
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From Zero Sales to Survival: Forecast-triggered Decision-making in Ecotourism MSMEs Singgih Purnomo; Nurmalitasari Nurmalitasari; Nurchim Nurchim; Novemy Triyandari Nugroho
Shirkah: Journal of Economics and Business Vol. 11 No. 1 (2026)
Publisher : Universitas Islam Negeri Raden Mas Said Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22515/shirkah.v11i1.1108

Abstract

Ecotourism micro, small, and medium-sized enterprises (MSMEs) often face highly volatile demand characterized by frequent zero-sales days, strong seasonality, and exposure to external shocks. In such conditions, sustainability depends less on forecast accuracy and more on timely, low-cost operational decisions. This study examines how forecast-triggered decision-making supports short-run viability under intermittent, zero-heavy demand. Using manually recorded daily sales data from ecotourism MSMEs in Tawangmangu, Indonesia, a two-stage approach is applied that separates sale occurrence from sales magnitude. First, a logistic model estimates the probability of a sale to generate early-warning signals. Second, conditional sales magnitude is predicted to indicate readiness levels rather than precise revenue targets. Instead of focusing on accuracy alone, the analysis evaluates decision usefulness through time-ordered backtesting, emphasizing avoidable operating days and early-warning lead time. The results show that sale-occurrence signals effectively guide daily operating decisions, while magnitude forecasts support proportional readiness. The framework identifies a substantial share of avoidable operating days and provides several days of advance warning before prolonged zero-sales periods. This enables earlier cost control and capacity adjustment. The study contributes by offering a practical, human-in-the-loop decision framework that links demand uncertainty with adaptive actions using simple, manually recorded data.
Penguatan Irigasi Pertanian melalui Pendampingan Pemasangan Solar Panel di Desa Guli Nurmalitasari Nurmalitasari; Nurchim Nurchim
Duta Abdimas Vol. 5 No. 1 (2026): Duta Abdimas: Jurnal Pengabdian Masyarakat
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/sferp748

Abstract

Pertanian pedesaan di Desa Guli menghadapi kendala irigasi akibat ketergantungan pada pompa berbahan bakar fosil dan listrik konvensional yang rentan terhadap ketidakstabilan pasokan serta tingginya biaya operasional, terutama pada musim kemarau. Kegiatan pengabdian ini bertujuan memperkuat sistem irigasi melalui pendampingan pemasangan panel surya sebagai sumber energi pompa air agar pengairan lebih stabil, efisien, dan berkelanjutan. Metode yang digunakan bersifat partisipatif dan aplikatif melalui tahapan persiapan, implementasi, pendampingan, dan evaluasi pada periode Agustus–Oktober 2025 dengan melibatkan petani dan perangkat desa. Implementasi meliputi survei lokasi, pemasangan rangka dan modul panel surya, integrasi dengan pompa air, uji fungsi, serta pelatihan pengoperasian dan pemeliharaan rutin. Hasil kegiatan menunjukkan sistem irigasi tenaga surya dapat dioperasikan secara efektif sehingga distribusi air lebih terjadwal dan tidak bergantung pada listrik konvensional. Dari sisi ekonomi, penerapan sistem ini menunjukkan adanya penurunan signifikan pada biaya operasional irigasi setelah penggunaan panel surya. Implikasi kegiatan ini adalah peningkatan keandalan irigasi, penguatan kapasitas petani dalam pengelolaan teknologi, serta efisiensi biaya energi yang mendukung keberlanjutan usaha tani dan peluang replikasi pada lahan lain di Desa Guli
Empowering Asian Students Through Artificial Intelligence: A Workshop on Predicting Plant Growth to Support Smart Farming Nurchim Nurchim; Nurmalitasari Nurmalitasari
International Journal Of Community Service Vol. 5 No. 1 (2025): February 2025 (Indonesia - Malaysia)
Publisher : CV. Inara in Colaboration with www.stie-sampit.ac.id

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijcs.v5i1.839

Abstract

The integration of Artificial Intelligence (AI) in agriculture has revolutionized traditional farming practices, enhancing productivity, efficiency, and sustainability. This study highlights a workshop aimed at equipping students with practical AI skills, specifically focusing on linear regression techniques for crop growth prediction. The workshop, involved 55 students from nine Asian countries, fostering cross-cultural collaboration. Participants were introduced to theoretical concepts and engaged in hands-on training, covering data preprocessing, region of interest extraction, and model implementation using Python. The program emphasized the role of AI in addressing agricultural challenges such as resource optimization and food security. The workshop was conducted in five stages: preparation, implementation, evaluation, dissemination, and participant engagement. Pre and post-test evaluations revealed a significant improvement in participants’ AI knowledge, with average scores increasing from 45% to 85%. Practical activities enabled students to connect theoretical knowledge with real-world applications, enhancing their ability to predict crop growth using AI techniques. Dissemination efforts included reports and publications to inspire similar global initiatives. The results demonstrated the workshop's effectiveness in bridging knowledge gaps, fostering sustainable agricultural practices, and preparing a skilled workforce capable of leveraging AI to address future challenges in smart farming.
Comparison of Content-Based Filtering and K-Nearest Neighbor in Genre-Based Movie Recommendations Irfan Nur Sofiyanto; Nurmalitasari Nurmalitasari; Rudi Susanto
RESWARA: Jurnal Riset Ilmu Teknik Vol. 4 No. 3 (2026): RESWARA: Jurnal Riset Ilmu Teknik, July 2026
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Kalibra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/reswara.v4i3.670

Abstract

The rapid growth of digital movie platforms has increased the number of available movies, making it difficult for users to find films that match their preferences. This study aims to implement and compare Content-Based Filtering and K-Nearest Neighbor methods in a genre-based movie recommendation system. The data used in this study were obtained from the Full TMDB Movies Dataset from Kaggle, consisting of 1,422,047 initial records. The dataset was processed through several stages, including attribute selection, missing value handling, genre transformation, duplicate data removal, and feature weighting using TF-IDF, resulting in 796,425 movie records ready for modeling. Content-Based Filtering was implemented using cosine similarity to calculate the similarity between user genre preferences and movie features, while K-Nearest Neighbor was applied by identifying movies with the closest distance to the user preference vector. The results show that both methods were able to generate relevant movie recommendations based on the selected genres, namely Action, Adventure, and Science Fiction. Based on the evaluation results, both methods achieved the same accuracy of 99.92%. However, K-Nearest Neighbor showed slightly better performance, with a precision of 86.56%, recall of 80.32%, and F1-score of 83.32%, compared to Content-Based Filtering, which achieved a precision of 86.45%, recall of 79.58%, and F1-score of 82.87%. These findings indicate that K-Nearest Neighbor is slightly more effective in identifying relevant movie recommendations based on user genre preferences.
SISTEM PREDIKSI VOLUME PENUMPANG HARIAN KRL YOGYAKARTA-SOLO MENGGUNAKAN MODEL HYBRID SARIMAX-PROPHET Muhammad Ilham 'Aziiz Alfarobi; Nurmalitasari; Ratna Puspita Indah
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.5721

Abstract

The operation of the Yogyakarta-Solo Commuter Line (KRL) since 2021 has become the backbone of transportation in the Yogyakarta Special Region and Central Java. However, highly dynamic fluctuations in passenger volume pose challenges for operational optimization. This research aims to develop an accurate daily passenger volume prediction system with a 30 day forecasting horizon to mitigate overcrowding and fleet inefficiency. The methodology employed is CRISP-DM, proposing a layered hybrid architecture based on residual modeling. In this model, SARIMAX serves as the primary pattern modeler (Layer 1), while Facebook Prophet acts as a residual corrector (Layer 2), optimized with selective correction mechanisms and daily adaptive weights. The research data covers the period from January 2025 to January 2026, totaling 396 observations. The test results show that the hybrid model provides the best performance compared to single models, achieving a Mean Absolute Percentage Error (MAPE) of 9.66% and a Mean Absolute Error (MAE) of 2,739 passengers per day. Utilizing historical data from January 2025 to January 2026, the modeling results are integrated into an interactive Streamlit dashboard as a practical decision support tool for KAI Commuter's proactive operational planning
PENERAPAN METODE HYBRID RANDOM FOREST DAN GENETIC ALGORITHM UNTUK OPTIMASI PENJADWALAN PRODUKSI Widya Monika Sari; Nurmalitasari; Bangun Prajadi Cipto Utomo
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.5789

Abstract

The garment industry faces complex production scheduling challenges due to high product variability and inaccurate process time estimation. Inefficient production scheduling often leads to production delays and reduced operational performance. Conventional scheduling methods such as First Come First Serve (FCFS) and Earliest Due Date (EDD) have been shown to be less effective in dynamic production environments. This study aims to optimize flow shop production scheduling in a children's garment manufacturing environment using a hybrid Random Forest–Genetic Algorithm approach. Random Forest is employed to predict the processing time of each job based on a simulated dataset regenerated from the company's historical production data collected in 2025 while preserving the statistical characteristics and relationships among variables. Subsequently, the Genetic Algorithm is used to optimize job sequencing by simultaneously minimizing makespan and weighted tardiness. The study follows the CRISP-DM methodology up to the model evaluation stage. The results show that the Random Forest model achieved satisfactory prediction performance for the cutting, sewing, and finishing stages, with R² values of 0.817, 0.981, and 0.867, respectively. Using a population size of 30, 100 generations, and 10 independent runs, the Genetic Algorithm achieved an improvement of 79.94% compared to FCFS and 39.71% compared to EDD. These findings demonstrate that the proposed hybrid Random Forest–Genetic Algorithm approach can generate a more adaptive, efficient, and data-driven production schedule than conventional scheduling methods.
PENERAPAN METODE K-MEANS CLUSTERING DAN SUPPORT VECTOR MACHINE (SVM) BERBASIS MODEL RFM UNTUK KLASIFIKASI TIER PELANGGAN Tariq; Nurmalitasari; Faulinda Ely Nastiti
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.5958

Abstract

Suboptimal management of large-scale transaction data can lead to marketing inefficiencies, particularly in determining promotional strategies that do not align with customer characteristics. This study aims to map the customer loyalty of CV Ekasa's client partners, by segmenting its customers using an integrated Recency, Frequency, Monetary (RFM) model, K-Means Clustering, and Support Vector Machine (SVM) classification. The dataset comprises 287,512 raw point-of-sale transaction records collected between October 2022 and September 2025, which after preprocessing yielded 341 valid customers for RFM modeling. Following the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, RFM features were log-transformed and standardized before clustering. Silhouette Score evaluation across k = 1–10 identified two customer segments (k = 2, Silhouette Score = 0.482) as optimal, labeled Passive Tier and Active Tier. These cluster labels were then used as classification targets for a linear-kernel SVM, evaluated under two data-splitting scenarios (80:20 and 70:30). The model achieved 97.10% accuracy with the 80:20 split and 98.06% with the 70:30 split, with precision, recall, and F1-scores above 0.97 for both tiers in both scenarios. These findings indicate that the integrated RFM–K-Means–SVM pipeline classifies customer loyalty tiers reliably and stably. The resulting model was deployed as an interactive Streamlit dashboard, giving CV Ekasa's client partner a practical, data-driven basis for designing more targeted and efficient marketing and retention strategies.
Pengembangan Model Rekomendasi Produk UMKM Albis Menggunakan Item Based Collaborative Filtering Muhammad Hafizh Al Mustofa; Nurmalitasari Nurmalitasari; Nurchim Nurchim
Buletin Sistem Informasi dan Teknologi Islam (BUSITI) Vol 5, No 2 (2024)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v5i2.2271

Abstract

UMKM atau Usaha Mikro, Kecil, dan Menengah adalah salah satu komponen penting yang mendukung perekonomian Indonesia, dijalankan oleh perorangan atau badan usaha dengan penghasilan tertentu setiap tahunnya. UMKM Albis, yang bergerak di bidang penjualan produk frozen food sejak tahun 2020 dan menawarkan 40 produk dengan berbagai merek. Banyaknya pilihan membuat pelanggan kesulitan dalam menentukan produk yang akan dibeli. Penelitian ini bertujuan memudahkan konsumen dalam memilih produk menggunakan model item-based collaborative filtering. Metode ini memberikan rekomendasi berdasarkan kemiripan antar produk menggunakan cosine similarity dan prediksi rating pengguna. Model ini menunjukkan kinerja yang baik dengan nilai MSE terendah sebesar 0,05416 dan RMSE sebesar 0,232724, Evaluasi tersebut menunjukkan bahwa model memiliki tingkat kesalahan yang rendah.
Co-Authors AA Sudharmawan, AA Abdullah Sajad Adenta Sari, Nur Avia Aditya Gema Pratama Afu Ichsan Pradana Agus Mardiyono Ahmad Tomi Aines Nafis Husna Alliya, Sania Qorrii Aprilisa Arum Sari Ardean Risqi Laksono Ashary Vermaysha Asmara Andhini Awang Long, Zalizah Bangun Prajadi Cipto Utomo Bima Setya Gumelar Dwi Hartanti Dwiki Rasya Rahadian Ega Muhammad Atsir Eko Purwanto Fadilah Nuria Handayani FAULINDA ELY NASTITI Hapsari, Theresia Pinaka Ratna Ning Hasanah, Herliyani Imam Sujadi Intan Diasih Irawan, Ridwan Dwi Irfan Nur Sofiyanto JOKO SUSANTO Lindha Juniarta Suseno Lucky Marsella Miftahul Hasanah Miftakhul Jannah, Ulfarida Moh Muhtarom Mohammad Yusuf Nugroho Muhammad Hafizh Al Mustofa Muhammad Ilham 'Aziiz Alfarobi Muhammad Ilham Arsyam Muhammad Rais Ramadhani Novemy Triyandari Nugroho Nugroho Arif Sudibyo Nugroho, Mohammad Yusuf Nurchim Permatasari, Hanifah Pipin Widyaningsih Rafel Fernando Rahmawati Desi Tri Wulandari Ratna Puspita Indah Rico Yoga Pradana Rifki Hidayat Rizqi Saputra Rudi Susanto Sajad, Abdullah Sari, Nur Avia Adenta Septi Dwi Supriati Setiawati, Neha Poetri Singgih Purnomo Sofa Marwati SRI SUMARLINDA Sulami, Atik Syaiful Nur Wardani Syawaludin, M Ainur Tariq Taufik Hidayat Tri Atmojo Kusmayadi Tri Wulandari, Rahmawati Desi Ulfarida Miftakhul Jannah Utami, Maulinda Devi Vihi Atina Viona Putri Ardiana Widya Monika Sari Yuliana Dewi Proboningrum Yusmawan Dwi Suseno Zalizah Awang Long Zalizah Awang Long