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All Journal JURNAL SISTEM INFORMASI BISNIS Jurnal Simetris JTET (Jurnal Teknik Elektro Terapan) E-Dimas: Jurnal Pengabdian kepada Masyarakat JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal DISPROTEK Abdimas Universal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Dinda : Data Science, Information Technology, and Data Analytics Jurnal Minfo Polgan (JMP)
Nur Aeni Widiastuti
Universitas Dian Nuswantoro
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Pengembangan Sistem Informasi Inventori Berbasis Java Pada CV. Bagaskara Galih Perkasa Jepara Sucipto, Adi; Setiawan, Fendi; Kusumodestoni, R. Hadapiningradja; Widiastuti, Nur Aeni
JTET (Jurnal Teknik Elektro Terapan) Vol 7, No 1: (April 2018)
Publisher : Teknik Elektro - Politeknik Negeri Semarang

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

Abstract

CV. Bagaskara Galih Perkasa Jepara is one of the companies engaged in furniture. Current inventory systems still have many shortcomings and weaknesses that cause the companys performance to be less than the maximum. There is inefficient data recording process because the same data recording is done several times because the form is available separately, and the absence of notification "goods are running out" on the current system that causes the delay of decision-making process. The purpose of this study is to develop an inventory information system that can manage data efficiently and facilitates the decision-making process. Object-oriented system development methodology using Rapid Application Development (RAD) method is utilized in this research and it is tested using black box testing and questionnaire. It is developed by system modeling using Unified Modelling Language (UML), Java programming language using Netbeans IDE tools, and MySQL database. Theresult of this research is a computerized inventory information system that can be used efficiently, and features of goods receiving, goods spending, goods inventory report, notice of near-expense supplies, and daily reports. Based on the results of research conducted to measure the feasibility of this product by spreading the questionnaire to thematerialexpert, the rate of user satisfaction is obtained 83.03% and the black box testing conducted by the media expert obtained 93.38%, it can be concluded that the inventory information system is included in the "very eligible" category to use.Keywords :Inventory, Rapid Application Development (RAD), Unified Modeling Language (UML), Java, MySQL, Black box testing
PENGEMBANGAN MEDIA ANIMASI KOMPUTER UNTUK MENINGKATKAN HASIL BELAJAR SISWA KELAS V SD UNGGULAN MUSLIMAT NU KABUPATEN KUDUS Widiastuti, Nur Aeni
Jurnal DISPROTEK Vol 9, No 2 (2018)
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jdpt.v9i2.729

Abstract

The success of a learning process influenced by teachers, learning materials, media and student. The problem of this research is developing Media Computer Animation How to improve student learning outcomes in class V Elementery School of Muslimat NU Kudus. Computer animation as a medium of instruction in intermediate teachers deliver the message because the use of this software is more practical, efficient, can be self-learning and also have some educational value, such as: 1) Can be visualized, 2) to overcome the limitations of space and time, 3) Can reduce verbal. This study aims to determine improving student learning outcomes in English language learning. This research uses classroom action research conducted in two cycles. In the first ,which in practice only teachers use classroom action research methods and not using the media increased by 5.4 from the pretest 12.36 and posttest 17.76. Then in the second which in its implementation with a class action research methods and using computer animation media also increase 4.11 from pretest 21 and postest 25.11. As for the observation of the first cycle included into the category of less and the second cycle included good categories
Teknologi Geolocation Berbasis Android dengan Metode K-Means untuk Pemetaan UMKM di Kabupaten Jepara Azizah, Noor; Widiastuti, Nur Aeni
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 8, No 2 (2018): Volume 8 Nomor 2 Tahun 2018
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (25.618 KB) | DOI: 10.21456/vol8iss2pp218-224

Abstract

Micro, Small and Medium Enterprises (MSMEs) are industrial sectors that are very important to sustain the economy of Jepara Regency. There are 18,695 Small and Medium Enterprises (SMEs) in Jepara Regency in 2016, including wood carving, troso weaving, chopper brass (monel) jewelry, sculpture, rattan crafts, calligraphy, and reliefs. The number of SMEs in Jepara makes buyers or tourists have many choices in buying products of varying quality and competitive prices. In addition, sometimes they are also confused in finding the location of SMEs. Therefore, this application is made to solve these problems by making an application that provides location-based information center industrial services. This application is expected to facilitate tourists in finding the location of the industry to be addressed. Geolocation technology is used to identify real-world geographic locations that can be applied to the Android operating system. So this application provides store description services, product photos, and maps. SMEs are presented in the application in the map using the k-mean algorithm. The parameters used are the type of industry, number of employees, turnover per year, tools used. For the clustering have 3 categories, there are namely small, medium and large. The advantages of this algorithm can group data according to the similarity of data used in one group and minimize the same data between groups and cannot process data that is a missing value.
Implementasi Algoritma K-Nearest Neighbor untuk Klasifikasi Jurusan pada Peserta Didik Baru Widiastuti, Nur Aeni; Azhar, Maulana; Mulyo, Harminto
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 14, No 2 (2023): JURNAL SIMETRIS VOLUME 14 NO 2 TAHUN 2023
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v14i2.10092

Abstract

Majoring students is a process of placing students into certain majors in accordance with their interests and academic abilities in an effort to make it easier for students in the learning process. Madrasah Aliyah Darul Hikmah Menganti is a school equivalent to SMA, which has two majors, namely science and social studies. The difficulty of classifying the majors of new students is an obstacle for the school. Because the criteria assessment process is carried out one by one. From these problems, the K-Nearest Neighbor (K-NN) method was applied to classify majors in order to simplify and minimize errors in the process of determining new student majors. The data initially amounted to 638 records and 31 attributes. After preprocessing, the data used amounted to 635 records with 12 attributes, namely name, gender, major interest, school origin, children to, number of siblings, math scores, English grades, science grades, Indonesian language scores, test scores, and major recommendations. After testing using K-Fold Cross Validation and Confusion Matrix for evaluation and validation of results by calculating the Euclidean Distance distance, the best k value (optimal) k=3 which produces accuracy: 97.11%, precision: 96.82%, recall: 98.33%, and AUC: 0.951.
Peningkatan Ketrampilan Pengrajin Mebel Melalui Pelatihan Perancangan Produk dari Limbah Hasil Produksi Arifin, Zaenal; Widiastuti, Nur Aeni; Zainudin, Achmad; Widodo, Agustina; Niamirroykhan, Achmad Syafiul; Najid, Muhammad Reza Ahsanun; Saputra, Reihan; Artalopa, Ryan
Abdimas Universal Vol. 7 No. 1 (2025): April
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Balikpapan (LPPM UNIBA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36277/abdimasuniversal.v7i1.1910

Abstract

Jepara Regency's leading products are carvings and furniture. These superior products are concentrated in Ngabul Village and Sukodono Village, Tahunan District, Jepara Regency. The partners in this service are Srijaya Indofurniture and I-Design Furniture which operates in the furniture sector and is already on an export scale. Even though partners have exported, they also experience several problems, one of which is the management aspect, namely: a) there are still difficulties in analyzing production planning so that a lot of raw materials are wasted and b) lack of partner knowledge in processing wood waste into products of marketable value. The methods used to overcome this problem are socialization of activities, training on processing furniture waste, mentoring, monitoring and evaluation of activities. The results of this activity are for partner employees to increase their knowledge in processing furniture waste into products with sales value and aesthetics.
SISTEM INFORMASI IDENTIFIKASI KAIN TENUN BERBASIS MOBILE PADA KAWASAN INDUSTRI TENUN IKAT JEPARA Sarwido, Sarwido; Widiastuti, Nur Aeni; Reviyandi, Riky
Jurnal Disprotek Vol 15, No 2 (2024)
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jdpt.v15i2.6772

Abstract

Tenun Ikat Troso atau Kain Ikat Troso adalah kriya tenun Jepara tepatnya dari Desa Troso. Tenun Ikat Troso berupa kain yang ditenun dari helaian benang pakan atau benang lungsin yang sebelumnya diikat dan dicelupkan ke dalam zat pewarna alami. Di Sentra Tenun Ikat ini pengunjung tidak hanya berwisata atau sekedar membeli sepotong kain saja, tetapi juga dapat mempelajari Kain-kain juga, sayangnya informasi yang ada hanya melalui deskripsi yang diberikan oleh penjaga toko yang sedang menjaga. Sayangnya kalau pengunjung toko tiba-tiba melonjak, penjaga kewalahan kalau dicecar pertanyaan oleh banyak pengunjung. Oleh sebab itu dengan dibuatkannya aplikasi ini diharapkan nantinya akan mempermudahkan tugas penjaga yang sedang dalam bertugas. Metode yang digunakan dalam perancangan ini menggunakan metode prototype, yang dapat digunakan untuk menghubungkan ketidakpahaman client. Pengunjung bisa melakukan scan ke Qr code yang sudah disediakan dengan menggunakan smartphone masing-masing dan bisa mendapatkan informasi tentang deskripsi kain. Penelitian ini menghasilkan sebuah Sistem Informasi Identifikasi Kain Tenun Berbasis Mobile yang menyediakan layanan berupa informasi jenis dan motif kain yang jelas di Kawasan Industri Tenun Ikat Jepara.MOBILE-BASED WOVEN FABRIC IDENTIFICATION INFORMATION SYSTEM IN THE JEPARA IKATE WEANING INDUSTRIAL AREAIkat Weaving from Troso or Troso Ikat Fabric is a weaving craft from Jepara, precisely from Troso Village. Troso Ikat is a fabric woven from strands of weft yarn or lungsin yarn that have been tied and dipped into natural dyes. In this Ikat Weaving Center, visitors can not only enjoy tourism or simply buy a piece of fabric but also learn about the fabrics. Unfortunately, the available information is only provided through descriptions given by the shopkeepers on duty. Regrettably, when the number of shop visitors suddenly increases, the shopkeepers are overwhelmed with questions from many visitors. Therefore, with the creation of this application, it is hoped that it will facilitate the duties of the shopkeepers on duty. The method used in this design uses the prototype method, which can be used to bridge the gap of understanding with the clients. Visitors can scan the provided QR code using their smartphones and get information about the fabric's description. This research resulted in a Mobile-Based Woven Fabric Identification Information System that provides services in the form of clear information about the types and patterns of fabric in the Jepara Ikat Weaving Industrial Area. 
Optimasi Algoritma K-Nearest Neighbors Menggunakan Teknik Bayesian Optimization Untuk Klasifikasi Diabetes Sowabi, Nur Kholis; Widiastuti, Nur Aeni; Maori, Nadia Annisa
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5975

Abstract

Diabetes is one of the chronic diseases that affects millions of people worldwide. Early diagnosis is crucial to prevent long-term complications, but the main challenges lie in the complexity of medical data and selecting optimal parameters for classification algorithms. This research aims to optimize the K-Nearest Neighbors (KNN) algorithm using Bayesian Optimization to improve accuracy in diabetes classification. The dataset used is the "Early-stage Diabetes Risk Prediction" from the UCI Machine Learning Repository, preprocessed through normalization and categorical feature encoding. Bayesian Optimization was applied to find the optimal parameters, such as the number of neighbors (k) and the best distance metric. The results show that the optimized KNN achieved 91.34% accuracy, 100% precision, and a 93.23% F1-Score, demonstrating a significant improvement over the standard KNN model. In conclusion, KNN optimization with Bayesian Optimization proves effective in enhancing diabetes classification performance and can contribute significantly to early detection and disease management.
Penerapan Metode Certainty Factor Pada Sistem Pakar Diagnosa Penyakit Kepiting Bakau di BBPBAP Jepara Basyar, Muhammad Irfan; Sucipto, Adi; Widiastuti, Nur Aeni
Jurnal Minfo Polgan Vol. 13 No. 1 (2024): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v13i1.14004

Abstract

Penelitian ini mengkaji penerapan metode Certainty Factor dalam pengembangan sistem pakar untuk mendiagnosa penyakit kepiting bakau di Balai Besar Perikanan Budidaya Air Payau (BBPBAP) Jepara. Kepiting bakau, komoditas bernilai ekonomi tinggi, rentan terhadap berbagai penyakit yang dapat merugikan pembudidaya. Tujuan utama sistem pakar ini adalah membantu pembudidaya dan staf BBPBAP mengidentifikasi penyakit kepiting bakau secara cepat, akurat, dan efisien. Metode Certainty Factor dipilih karena kemampuannya menangani ketidakpastian dalam diagnosis penyakit, dengan mempertimbangkan tingkat keyakinan pakar dan bukti yang ada. Sistem ini menggunakan basis pengetahuan komprehensif dari analisis data gejala dan penyakit kepiting bakau dengan ahli BBPBAP Jepara serta studi literatur. Pengembangan meliputi akuisisi pengetahuan, perancangan basis aturan, implementasi metode Certainty Factor, dan pengujian sistem. Hasil pengujian menunjukkan diagnosis akurat dengan tingkat kepercayaan terukur, Penelitian ini menghasilkan peningkatan signifikan dalam akurasi diagnosis, dengan sistem yang dikembangkan mencapai tingkat akurasi sebesar 96.17%. Hasil ini dianggap sebagai kemajuan substansial dalam bidang tersebut. Penggunaan metode Certainty Factor pada sistem pakar terbukti efektif dalam meningkatkan keakuratan diagnosis penyakit pada kepiting bakau. Hasil penelitian ini menunjukkan potensi yang menjanjikan untuk meningkatkan manajemen kesehatan dalam budidaya kepiting bakau, yang dapat berdampak positif pada industri perikanan air payau. serta menawarkan rekomendasi penanganan awal. Implementasi sistem ini diharapkan mendukung pengambilan keputusan cepat dan tepat, meningkatkan kesehatan dan produktivitas budidaya kepiting bakau.
Comparison of Accuracy of Linear Regression and Random Forest Models in Predicting Bitcoin Prices Awwaluddin, Ahmad Habib; Tamrin, Teguh; Widiastuti, Nur Aeni
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i2.2014

Abstract

Abstract Bitcoin is a digital asset that has experienced significant growth in value since its launch in 2009. However, its high price volatility makes predicting Bitcoin's price movements a challenge for investors and financial analysts. Therefore, a data-driven approach capable of capturing patterns in historical Bitcoin price data is needed to support more accurate investment decision-making. This study aims to evaluate and compare the performance of two prediction algorithms, namely Linear Regression and Random Forest, in predicting Bitcoin prices based on daily historical data from 2018 to 2025. The dataset was obtained from the Kaggle platform and processed through pre-processing, predictive feature formation, and data normalization. Two validation schemes were used: a 70:30 data split and cross-validation using K-Fold Cross Validation (10-fold). Model performance evaluation was carried out using three main metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results show that the Linear Regression model produces better performance than Random Forest, both on split data and cross-validation, even though Random Forest has been optimized using GridSearchCV. The lowest RMSE value was obtained from Linear Regression in the K-Fold scheme, at 1314.47. These findings indicate that a simple model such as Linear Regression can still be effective in predicting Bitcoin prices if the data is properly processed. This research is expected to serve as a reference for developers of digital asset price prediction systems and stakeholders in data-driven decision-making.. Keywords: Bitcoin, Prediksi Harga, Regresi Linier, Random Forest, Evaluasi Model, Machine Learning, K-Fold Cross Validation
Comparison of Support Vector Machine (SVM) and Random Forest Algorithms in the Analysis of SOcial Media X User Sentiment Towards the TNI Bill Rochmawati, Nur; Zyen, Akhmad Khanif; Widiastuti, Nur Aeni
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10883

Abstract

The rapid advancement of information technology has enabled the public to openly express their views through social media, including on strategic national issues such as the Draft Law on the Indonesian National Armed Forces (RUU TNI). This study aims to map public sentiment toward the RUU TNI and to compare the effectiveness of two popular sentiment analysis algorithms, Support Vector Machine (SVM) and Random Forest (RF). A total of 525 relevant tweets collected between February and May 2025 were analyzed and classified into three sentiment categories: positive, negative, and neutral. The results reveal that neutral opinions dominate at 81.4%, followed by negative sentiments at 11.1% and positive sentiments at 7.4%. The performance comparison shows that SVM achieved an accuracy of 92%, outperforming RF which obtained 91%. These findings highlight that strategic defense issues tend to generate predominantly informative public opinions, while critical voices show an increasing trend as the discourse evolves. The novelty of this study lies in the application of three-class sentiment classification and the comparative evaluation of SVM and RF within the domain of defense policy. This research contributes to the academic discourse by extending sentiment analysis beyond electoral and marketing topics, while also providing practical insights for policymakers in understanding and responding to public aspirations more effectively.
Co-Authors A.A. Ketut Agung Cahyawan W Achmad Zainudin, Achmad Adi Sucipto, Adi Agustina Widodo Akhmad Khanif Zyen, Akhmad Khanif Artalopa, Ryan Awwaluddin, Ahmad Habib Azhar, Maulana Basyar, Muhammad Irfan Maori, Nadia Annisa Mulyo, Harminto Najid, Muhammad Reza Ahsanun Niamirroykhan, Achmad Syafiul Noor Azizah R Hadapiningradja Kusumodestoni Reviyandi, Riky Rochmawati, Nur Saputra, Reihan Sarwido Sarwido, Sarwido Setiawan, Fendi Sowabi, Nur Kholis Teguh Tamrin Zaenal Arifin