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Tofu Dregs Crackers, Innovation of Industrial Solid Waste Utilization Know Ampeldento Village, Malang Regency Trisna Martha Nur Susila Kusuma Candra; Rifki Muhamad Azam; Intan Isnaeni; Nurul Maulida Ulviah; Syaidatul Fiza Ma'arif; Nur Fitriyah Ayu Tunjung Sari
Dedikasi: Jurnal Pengabdian kepada Masyarakat Vol 16 No 1 (2023): Januari-Juni
Publisher : Pusat Pengabdian Kepada Masyarakat Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32678/dedikasi.v16i1.7910

The tofu industry located in Ampeldento Village, Malang Regency is one of the scale industries MSMEs in the surrounding environment which were founded in 2017. Every day, this industry is able to produce 100 kg tofu with 25 kg of solid waste which is often called tofu dregs. Currently solid waste generated from Tofu production process has been used as a mixture of animal feed. Utilization of solid waste that has not been optimal results in a large volume of tofu solid waste that is wasted. Where does it arise problems in the surrounding environment, namely the emergence of unpleasant odors around the waste disposal site. Based on these conditions required new innovations in the utilization of tofu solid waste. Through the dedication program university community, an innovation is made to utilize tofu solid waste into processed crackers. which method applied to the implementation of service is a society participant. The formulated innovation is expected to be able be a solution to existing conditions. In addition, tofu dregs crackers are expected to become a new commodity to add value to the tofu industry in Ampeldento Village.

Utilizing the game design factor questionnaire to develop engaging games for adaptive learning in the serious educational game: the Ma'had Sari, Nur Fitriyah Ayu Tunjung; Kusumawati, Ririen; Karami, Ahmad Fahmi; A, Miftahul Hikmah Putri Samudera
OPSI Vol 17, No 1 (2024): ISSN 1693-2102
Publisher : Jurusan Teknik Industri Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v17i1.11322

This study explores the unique mandatory residence, Ma'had Sunan Ampel Al Alyi (MSAA), at the State Islamic University (UIN) Maulana Malik Ibrahim Malang, aiming to develop Quranic reading competence in new students. The varying educational backgrounds of admitted students lead to differences in their Quranic reading capabilities, highlighting the need for adaptive learning. In response to this diversity, adaptive learning using artificial intelligence is employed, implemented through the serious education game "The Ma'had." Survey results from expert individuals using a Game Design Factor Questionnaire reveal the game's substantial potential. The results show high agreement (100%) on clear goals, engaging gameplay, and a sense of freedom, with 67% strongly agreeing on improved understanding. Challenges are motivating, and the game successfully sparks curiosity. "The Ma’had" Game proves effective, but further research is recommended to explore variations in player engagement and compare results with expert test subjects, employing alternative quantitative testing methods for a comprehensive analysis.

Utilizing the K-Means Algorithm for Breast Cancer Diagnosis: A Promising Approach for Improved Early Detection Fitriyah Ayu Tunjung Sari, Nur; Nabela, Maharini; Falah Abdurrohman, Muhammad
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 15, No 2 (2023): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v15i2.23644

Breast cancer is a pressing non-communicable disease, especially affecting women, with its incidence on the rise. In 2020, it ranked among the most common cancers in Indonesia. Timely detection and precise diagnosis are pivotal for effective breast cancer management. To enhance diagnostic accuracy, the K-means clustering method is applied to group patients based on shared attributes. This research aims to contribute significantly to breast cancer diagnosis by leveraging the K-means method, potentially improving patient survival rates.The research process involves data collection, preprocessing, K-means application, evaluation, and visualization. A dataset of 569 breast cancer patient records with 32 attributes from Kaggle is utilized. The K-Means algorithm is assessed using accuracy, yielding a value of 0.8457, signifying good performance. Malignant cases (211) and benign cases (301) are visualized in a scatter plot, distinguishing between them.In conclusion, this study presents an initial step in utilizing the K-means algorithm for breast cancer diagnosis, offering promising results. Further research and the development of more advanced models are imperative to address the global health challenge posed by breast cancer among women.Index Terms—breast cancer; clustering; K-Means Algorithm

Analisis dan Optimalisasi Performa Algoritma Gaussian Naive Bayes pada Prediksi Metabolic Syndrome Menggunakan SMOTE Fauziyah, Nadiyah Jihan; Rahmania, Fadilla; Daniyal, Muhammad; Sari, Nur Fitriyah Ayu Tunjung
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 2 (2024): Mei 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.2.112-122

Metabolic syndrome is a complex global health problem, with symptoms such as abdominal obesity, insulin resistance, high blood pressure, high blood sugar, and abnormal blood lipids. With this global challenge, several studies have attempted to predict these diseases using machine learning methods. However, often, predictions about a disease result in data imbalance where minority classes are underrepresented. To balance the class proportions, the Synthetic Minority Over-sampling Technique (SMOTE) method replicates the minority class samples. In this research, the technique applied to predict is the Gaussian Naive Bayes (GNB) algorithm. The results show an increase in prediction accuracy by 0.2 from 0.81 to 0.83. This study confirms the critical role of the SMOTE oversampling method in machine learning using the Gaussian Naive Bayes (GNB) algorithm in Metabolic Syndrome prediction and its positive impact on diagnostic efficiency and public health.

Pemberdayaan Rumah Tangga Melalui Praktik Home Garden Sebagai Upaya Pencegahan Stunting di Desa Gondang Wetan Retnasih, Nora Ria; Sari, Nur Fitriyah Ayu Tunjung; Septiana, Yuvi Putri
Abdimas Galuh Vol 7, No 1 (2025): Maret 2025
Publisher : Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/ag.v7i1.16585

Kegiatan pengabdian masyarakat ini bertujuan untuk mencegah stunting melalui pemberdayaan rumah tangga dengan praktik home garden di Desa Gondang Wetan, Kabupaten Pasuruan, menggunakan pendekatan Asset-Based Community Development (ABCD). Pendekatan ini dilakukan melalui lima tahapan: Discovery (menemukan) potensi lokal berupa lahan pekarangan yang tidak termanfaatkan dan kondisi stunting yang masih tinggi; Dream (impian), yaitu merancang solusi berupa home garden untuk meningkatkan gizi keluarga; Design (merancang) kegiatan yang melibatkan sosialisasi, praktik menanam sayuran, dan monitoring; Define (menentukan) peran warga sebagai role model dalam pelaksanaan praktik; serta Destiny (melakukan), yaitu implementasi program dengan antusiasme warga. Hasil kegiatan menunjukkan bahwa praktik home garden mampu meningkatkan akses rumah tangga terhadap sayuran bergizi, memperkuat kesadaran masyarakat akan pentingnya gizi dalam mencegah stunting, serta memanfaatkan aset lokal secara efektif. Monitoring juga menunjukkan keberhasilan beberapa keluarga dalam memanen dan memanfaatkan hasil tanamannya. Program ini menciptakan dampak jangka panjang yang mendukung kesejahteraan berkelanjutan melalui peningkatan kapasitas masyarakat dalam mengelola sumber daya secara mandiri. Implikasi dari kegiatan ini diharapkan dapat menjadi model pemberdayaan yang berkelanjutan di desa lain dengan tantangan serupa.

Utilizing the game design factor questionnaire to develop engaging games for adaptive learning in the serious educational game: the Ma'had Sari, Nur Fitriyah Ayu Tunjung; Kusumawati, Ririen; Karami, Ahmad Fahmi; A, Miftahul Hikmah Putri Samudera
OPSI Vol 17 No 1 (2024): ISSN 1693-2102
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v17i1.11322

This study explores the unique mandatory residence, Ma'had Sunan Ampel Al Alyi (MSAA), at the State Islamic University (UIN) Maulana Malik Ibrahim Malang, aiming to develop Quranic reading competence in new students. The varying educational backgrounds of admitted students lead to differences in their Quranic reading capabilities, highlighting the need for adaptive learning. In response to this diversity, adaptive learning using artificial intelligence is employed, implemented through the serious education game "The Ma'had." Survey results from expert individuals using a Game Design Factor Questionnaire reveal the game's substantial potential. The results show high agreement (100%) on clear goals, engaging gameplay, and a sense of freedom, with 67% strongly agreeing on improved understanding. Challenges are motivating, and the game successfully sparks curiosity. "The Ma’had" Game proves effective, but further research is recommended to explore variations in player engagement and compare results with expert test subjects, employing alternative quantitative testing methods for a comprehensive analysis.

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