Article Per Year (5 Year)
p-Index From 2020 - 2025
0.562
P-Index
This Author published in this journals
All Journal Jurnal Dedikasi Indonesian Journal of Information System Journal of Software Engineering and Information System (SEIS) Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
Ida Widaningrum
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Environmental Science Languange, Linguistic, Communication & Media Public Health

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
TEKNOLOGI PEMBUATAN TAHU YANG RAMAH LINGKUNGAN (BEBAS LIMBAH) Ida Widaningrum
Jurnal Dedikasi Vol. 12 (2015): Mei
Publisher : Direktorat Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/dedikasi.v12i0.2476

Abstract

TEKNOLOGI PEMBUATAN TAHU YANG RAMAH LINGKUNGAN (BEBAS LIMBAH)Ida WidaningrumFakultas Teknik,Universitas Muhammadiyah Ponorogoemail:iwidaningrum@yahoo.co.idABSTRAKTahu memiliki protein nabati kualitas terbaik karena memiliki komposisi asam amino paling lengkap dan diyakini memiliki daya cerna yang tinggi (sebesar 85% -98%). Kitan mengetahui bahwa tahu selama ini menggunakan cuka, dengan biaya yang relatif rendah. Tapi tahu ini memerlukan banyak air dalam proses manufaktur, limbah tahu selama ini sangat mengganggu lingkungan sekitar, serta efek cuka dapat menyebabkan asam lambung. Dengan perkembangan teknologi, ada bahan yang bisa menggantikan cuka untuk membekukan bubur kedelai, zat ini tidak menimbulkan bau serta tidak melepaskan limbah, dan tidak membutuhkan tempat yang luas. Jenis ini dikenal sebagai Tofu tahu nigarin dimana proses pembuatannya menggunakan air laut Nigarin atau sari. Nigarin tahu yang diproses tanpa limbah, tidak berbau, sehingga proses manufaktur yang ramah lingkungan. Selain itu, mikro Nigarin mengandung mineral yang dibutuhkan oleh tubuh. Berisi lebih dari 80 jenis mineral, termasuk Magnesium, Kalium, Besi, Kalsium, Boron, Selenium, dan Zinc. Tujuan IbM adalah untuk memperkenalkan dan praktek membuat tahu menggunakan Nigarin ini kepada produsen tahu, masyarakat dan komunitas bisnis warung makanan (dalam hal ini kantin sekolah). Metode yang digunakan dalam kegiatan pengabdian ini adalah sosialisasi, diskusi, pelatihan dan pendampingan cara membuat tahu ramah lingkungan, pembuatan makanan / minuman yang berasal dari ampas tahu, evaluasi dan monitoring. Hasilnya cukup menarik perhatian dan minat masyarakat dengan indikator keberhasilan peningkatan pengetahuan tentang cara lain untuk menghasilkan tahu yang sehat dan lebih praktis. Pengetahuan tentang penggunaan nigarin yang bisa berfungsi untuk menebalkan bubur kedelai sebagai pengganti cuka. Menambah pengetahuan tentang bahaya pencemaran lingkungan yang disebabkan oleh limbah.Kata kunci: Tahu, Sumber Protein, Cuka, Nigarin, Ramah Lingkungan
SUPPORT VECTOR MACHINE ALGORITHM FOR EARLY DETECTION SYSTEM FOR MENTAL EMOTIONAL DISORDERS IN ADOLESCENTS Muthya Cahyani Putriabhimata; Ida Widaningrum; Dyah Mustikasari
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 1 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i1.8351

Abstract

A mental-emotional disorder is a condition characterized by emotional fluctuations that, if left untreated, might progress into an abnormal state. In Indonesia, the treatment of mental problems is infrequently conducted due to a scarcity of psychiatric personnel and the high expenses associated with comprehensive mental health therapy and treatment. An early detection system for mental-emotional illnesses in teenagers was developed by implementing the Support Vector Machine (SVM) algorithm as a solution to this issue. The Support Vector Machine algorithm is a very accurate classification approach. This study utilizes data that is categorized into two distinct groups: anxiety and depression. The data is partitioned in an 80:20 ratio, with 80% allocated for training data and 20% for test data. The research findings indicate that the testing accuracy levels yielded a value of 85%. The value is derived using the RBF kernel with a gamma value of 0.1 and a C value 10. The Support Vector Machine model is implemented within the Graphical User Interface (GUI). The user experience questionnaire was assessed on the Graphical User Interface, resulting in a user experience score within the "good" category.
Using SVM and KNN for Predicting Customer Response Sentiment of M-PAJAK Application Muhammad Titan Rama Adi Wijaya; Ida Widaningrum; Angga Prasetyo; Dyah Mustikasari
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 11 No. 1 (2025): April 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v11i1.4528

Abstract

M-Pajak, an application initiated by the Directorate General of Taxes, signifies the modernization of taxation and serves a crucial function. This application facilitates taxpayers in meeting their tax obligations. User satisfaction with this application may be assessed via reviews on the Google Play Store. While this application fulfills client satisfaction, its sustained success is significantly contingent upon user contentment and experience. Sentiment analysis is essential for elucidating user evaluations and interactions with the program. This research analyses the sentiment of M-Pajak application reviews on Google Play using Support Vector Machine (SVM) and K-Nearest Neighbour (KNN), supported by the Term Frequency-inverse Document Frequency (TF-IDF) feature extraction method. A total of 1000 reviews between December 11, 2022 and December 2, 2023 were processed using KNN and SVM. The KNN algorithm yielded 153 positive predictions and 847 negative predictions and achieved 94% of accuracy. Meanwhile, SVM achieved an accuracy of 88.10%, with 325 positive predictions and 675 negative predictions. The results demonstrate the superiority of KNN in sentiment classification of M-Pajak reviews. This study also indicates that negative comments outnumber positive ones in this application. This serves as a signal for the Directorate General of Taxation to enhance user satisfaction with the M-Pajak application through continuous updates.
Recognition of the Lima Pandawa Shadow Puppet characters utilizing Principal Component Analysis (PCA) for feature extraction and K-Nearest Neighbor (KNN) for classification Ida WIdaningrum; Indah Puji Astuti; Dyah Mustikasari; Khoiru Nurfitri; Rifqi Rahmatika Az-Zahra; Rhesma Intan Vidyastari; Ali Selamat
Indonesian Journal of Information Systems Vol. 8 No. 1 (2025): August 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v8i1.11032

Abstract

The traditional type of puppet-shadow play, Wayang Kulit, is an integral component of Indonesian culture. The Pandawa Lima, protagonists in this artistic medium, have great importance not just in narrative but also in embodying moral and ethical principles. The automated identification of these characters can optimize a range of applications, such as instructional resources, digital preservation, and interactive displays. This research intends to maximize the advantages of PCA and KNN by utilizing their respective strengths: PCA's capacity to decrease data dimensionality and KNN's efficacy in classification tasks. An expected outcome of this combination is an enhancement in recognition accuracy without compromising computational efficiency. The classification matrix indicates that the model achieved a 78% accuracy rate. Class-specific accuracy, recall, and F1-scores are as follows: arjuna achieves a precision of 0.85, recall of 0.91, and F1 Score of 0.87. Macro averages for precision, recall, and F1 are 0.77, 0.76, and 0.74, respectively. Weighted averages for these metrics are 0.80, 0.78, and 0.77, respectively. The model exhibits strong performances on Arjuna, Sadewa, and Yudistira, but encounters difficulties with Bima and Nakula.
Co-Authors Ali Selamat Angga Prasetyo Dyah Mustikasari Indah Puji Astuti Khoiru Nurfitri Muhammad Titan Rama Adi Wijaya Muthya Cahyani Putriabhimata Rhesma Intan Vidyastari Rifqi Rahmatika Az-Zahra