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Pengelolaan Sampah Berbasis Teknologi Informasi untuk Masyarakat Perkotaan Mulyawan; Khaerul Anam; Daffa Ezra Pratama; Dini Andriyani
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 04 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Urban waste management faces significant challenges due to the increasing volume of waste, inadequate infrastructure, low public awareness, and limited use of technology. This community service program aims to develop an information technology-based solution to create a more efficient, environmentally friendly, and community-engaged waste management system. Through the development of a mobile application, residents can report waste conditions in real-time, monitor waste sorting, and access waste collection schedules. The program also includes community training, provision of waste sorting facilities, and educational campaigns to raise environmental awareness. The implementation has shown significant improvements in public participation in waste sorting, reduction in the volume of waste sent to landfills, and overall improvement in environmental quality. Furthermore, this initiative contributes to human resource empowerment through training in technology use and waste management. The success of this program demonstrates that integrating information technology with public education can be an effective solution to urban waste management challenges.

Clustering Data Penjualan Produk Makanan pada Toko Toserba Yogya Siliwangi dengan Menggunakan Metode K-Means Noviati; Mulyawan; Kurnia, Dian Ade; Rinaldi, Ade Rizki
MEANS (Media Informasi Analisa dan Sistem) Volume 7 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

Product availability is one of the important factors to increase sales and maintain customer satisfaction in meeting their needs. With this, the company needs to analyze sales data, both for the best-selling products or those that are not selling well from sales reports every month, especially for food products. Of course, this is not easy, especially for a large enough retailer such as the Yogya Siliwangi Toserba which has thousands of product items and thousands of sales data every month. The above problems can be solved by grouping the data using the k-means clustering algorithm on rapidminer with variables taken by the name of goods, incoming goods, outgoing goods and stock. The goal is to maximize sales and maintain product stock availability to meet the diverse needs of consumers. From the calculation of the k-means algorithm using the rapidminer application, the results obtained are in the form of three clusters, cluster_1 3 items, cluster_2 13 items and cluster_0 454 items with Devies Bouldin results being 0.478.

Pengelolaan Aplikasi Layanan Administrasi pada Kelurahan Argasunya Garsandi, Akmal Maulana; Mulyawan; Dana, Raditya Danar; Kaslani; Tohidi, Edi
MEANS (Media Informasi Analisa dan Sistem) Volume 7 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

Argasunya Village is a combined area of several Rukun Warga (RW). Government at the village and sub-district levels is an element of government that is directly related to the community. Kelurahan is also an administrative division under the subdistrict. The village office has not used the system in making a Business Certificate. Therefore, the author tries to make a service application for making a business certificate based on a web application with the hope of simplifying the procedures for making a business certificate, the author uses data sources (Observation and Interviews). The programming language used is PHP language with MySql database. For system design, the author uses the Prototype method. From the results of the tests carried out, the system functionality can run well and the making of online and onsite business certificates is appropriate and can run well..

Potensi Ekstrak Bunga Kecombrang (Etlingera elatior) dan Tanaman Mimosa pudica L. sebagai Edible Coating untuk Memperpanjang Masa Simpan pada Buah Apel: Potential of Kecombrang Flower Extract (Etlingera elatior) and Plants Mimosa pudica L. as an Edible Coating to Extend the Shelf Life of Apples Mulyawan; Dian Indratmi; Erfan Dani Septia; Yusufa Alif Hidayat; Rovi Amallia Malikah
Jurnal Hortikultura Indonesia (JHI) Vol. 16 No. 1 (2025): Jurnal Hortikultura Indonesia (JHI)
Publisher : Indonesian Society for Horticulture / Department of Agronomy and Horticulture

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jhi.16.1.58-69

The apple industry in Indonesia, particularly in East Java Province, faces significant challenges related to fruit spoilage, which can result in substantial economic losses. One potential solution to address this issue is the application of natural-based edible coatings. This study aims to examine the effects of combining torch ginger (Etlingera elatior) flower extract and mimosa (Mimosa pudica) plant extract as the main ingredients in edible coatings on the quality and shelf life of apples. A Completely Randomized Design (CRD) was employed with four treatments: no coating (P0), coating with 2% extract (P1), 4% extract (P2), and 6% extract (P3). The results revealed that the combination of these extracts contained antimicrobial compounds such as dodecanal, octane, 1,1-diethoxy-, squalene, and methyl stearate, which effectively inhibited spoilage. The P3 treatment (6% extract) proved the most effective in maintaining apple quality, as indicated by stable weight, firmness, and sustained sugar and vitamin C content during storage. Keywords: post-harvest, storage capacity, secondary metabolites

Analisis Dan Prediksi Risiko Kelahiran Bayi Menggunakan K-Means Dan Deep Neural Network (DNN) Mukhlisin Ilahudin; Nana Suarna; Agus Bahtiar; Mulyawan; Irfan Ali
Jurnal Sistem Informasi dan Teknologi Vol 6 No 1 (2026): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v6i1.206

Risiko kelahiran bayi merupakan indikator penting dalam evaluasi kesehatan ibu dan anak sehingga diperlukan pendekatan analitis yang mampu mengidentifikasi pola risiko secara akurat. Penelitian ini bertujuan menganalisis dan memprediksi risiko kelahiran bayi dengan mengintegrasikan metode K-Means dan Deep Neural Network (DNN). Dataset yang digunakan terdiri dari 983 data rekam medis ibu hamil yang telah melalui tahap pengumpulan data, pembersihan, dan preprocessing meliputi normalisasi, encoding variabel kategorikal, penanganan outlier, serta seleksi fitur. Metode K-Means digunakan untuk mengelompokkan data berdasarkan kemiripan karakteristik klinis guna membentuk representasi pola risiko awal, yang selanjutnya digunakan sebagai fitur tambahan pada model DNN. Model DNN dirancang menggunakan beberapa hidden layer dengan fungsi aktivasi ReLU dan regularisasi dropout. Hasil pengujian menunjukkan bahwa model menghasilkan akurasi sebesar 61,93% dan nilai ROC AUC sebesar 0,6402, yang mengindikasikan performa moderat dalam memprediksi risiko kelahiran bayi. Stabilitas kurva loss dan akurasi menunjukkan proses pelatihan yang berjalan dengan baik tanpa overfitting signifikan. Secara praktis, model ini berpotensi digunakan sebagai alat bantu awal bagi tenaga kesehatan dalam mengidentifikasi ibu hamil dengan risiko kelahiran lebih tinggi sehingga dapat dilakukan pemantauan dan intervensi lebih dini.

Analisis Dan Prediksi Risiko Kelahiran Bayi Menggunakan K-Means Dan Deep Neural Network (DNN) Mukhlisin Ilahudin; Nana Suarna; Agus Bahtiar; Mulyawan; Irfan Ali
Jurnal Sistem Informasi dan Teknologi Vol 6 No 1 (2026): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v6i1.206

Risiko kelahiran bayi merupakan indikator penting dalam evaluasi kesehatan ibu dan anak sehingga diperlukan pendekatan analitis yang mampu mengidentifikasi pola risiko secara akurat. Penelitian ini bertujuan menganalisis dan memprediksi risiko kelahiran bayi dengan mengintegrasikan metode K-Means dan Deep Neural Network (DNN). Dataset yang digunakan terdiri dari 983 data rekam medis ibu hamil yang telah melalui tahap pengumpulan data, pembersihan, dan preprocessing meliputi normalisasi, encoding variabel kategorikal, penanganan outlier, serta seleksi fitur. Metode K-Means digunakan untuk mengelompokkan data berdasarkan kemiripan karakteristik klinis guna membentuk representasi pola risiko awal, yang selanjutnya digunakan sebagai fitur tambahan pada model DNN. Model DNN dirancang menggunakan beberapa hidden layer dengan fungsi aktivasi ReLU dan regularisasi dropout. Hasil pengujian menunjukkan bahwa model menghasilkan akurasi sebesar 61,93% dan nilai ROC AUC sebesar 0,6402, yang mengindikasikan performa moderat dalam memprediksi risiko kelahiran bayi. Stabilitas kurva loss dan akurasi menunjukkan proses pelatihan yang berjalan dengan baik tanpa overfitting signifikan. Secara praktis, model ini berpotensi digunakan sebagai alat bantu awal bagi tenaga kesehatan dalam mengidentifikasi ibu hamil dengan risiko kelahiran lebih tinggi sehingga dapat dilakukan pemantauan dan intervensi lebih dini.

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