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Ningrum, Novita Kurnia
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

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IoT-Based Prediction of Ornamental Plant Water Needs Using Sugeno Fuzzy Algorithm Dwitama, Reiza Hersa; Ningrum, Novita Kurnia
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9999

Abstract

Urban plant care is increasingly important amid growing concerns about air pollution and limited time for manual maintenance. In Indonesia, air quality has deteriorated significantly, with PM2.5 pollution levels exceeding World Health Organization standards, particularly in major cities like Jakarta. Ornamental plants play a crucial role in improving air quality; however, urban residents often struggle to consistently water them. This study addresses that problem by developing an Internet of Things (IoT)-based smart irrigation system that utilizes the Sugeno fuzzy algorithm to predict the water needs of ornamental plants. The system combines a capacitive soil moisture sensor and a DHT11 temperature-humidity sensor with an ESP8266 microcontroller to monitor environmental conditions. Data is transmitted to Firebase and visualized in an Android application, which provides real-time monitoring and specific volume recommendations ranging from 10 ml to 240 ml, calibrated for medium-sized plant pots which is also based on 27 fuzzy rules derived from three input parameters: air temperature, humidity, and soil moisture. Real-world testing with the Aglaonema Snow White plant confirmed that the system functions reliably, helping users optimize water usage and support sustainable, data-driven plant care in urban environments. The system achieved an average prediction accuracy of 89.14% and a mean absolute error of 7.6% in guiding soil moisture toward a 70% target, confirming its practical effectiveness. While the system was tested on Aglaonema Snow White, the fuzzy rule base can be recalibrated for other ornamental plant species with different water needs.
Analisis Sentimen Ulasan Game dengan KNN: Perbandingan Rating dan Kamus Sentimen Sunyaruri, Wisesa Sat; Ningrum, Novita Kurnia
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30133

Abstract

The growth of the global gaming industry makes sentiment analysis of user reviews a crucial tool for understanding satisfaction and identifying technical issues. This study aims to evaluate three labelling methods (rating-based, Sentiwords_id, and InSet) for classifying the sentiment of Indonesian-language reviews for the game Zenless Zone Zero (ZZZ) using the K-Nearest Neighbor (KNN) algorithm. The study analyzes 4,282 reviews from the Google Play Store, which underwent a Data Preprocessing stage, including Null Handling, Cleaning, Case Folding, Tokenization, Stopword Removal, and Stemming. The KNN's performance for each labelling method was evaluated using accuracy, precision, recall, and F1-score metrics on 80:20 train-test split. The labelling results reveal different sentiment perceptions: the rating-based method tends toward positive, InSet toward negative, while Sentiwords_id is dominated by the positive and neutral classes. The KNN performance evaluation shows that rating-based labelling achieved the highest accuracy (72%), excelling on the positive class (86% recall) but performing poorly on the neutral class (9% recall). Conversely, the lexicon-based labelling methods (both 69% accuracy) have specific strengths: InSet in negative detection (81% recall) and Sentiwords_id in recognizing the neutral class (83% recall). Main challenges of this study include the lexicon's limitations in handling slang and game-specific terms, as well as the inconsistency between ratings and text. This study is expected to provide empirical evidence on performance trade-offs among automatic labelling methods to aid in identifying player satisfaction and advancing the quality of game development.
Digitalisasi Pengelolaan Pemasaran Desa Wisata Banjaran Kabupaten Purbalingga Melalui Website Desa Wisata Banjaran Ningrum, Novita Kurnia; Susanto, Ajib; Mulyono, Ibnu Utomo Wahyu; Sudaryanto, Sudaryanto
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 3 (2025): Vol 8, No 3 (2025): SEPTEMBER 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i3.2988

Abstract

Desa Banjaran di Kecamatan Bojongsari Kabupaten Purbalingga memiliki potensi tinggi untuk meningkatkan perekonomian masyarakat melalui kekayaan alam dan budaya yang dimilik masyarakat setempat. Saat ini Desa Banjaran sudah memiliki Desa Wisata Banjaran, akan tetapi belum menjangkau pasar yang luas dikarenakan keterbatasan sumber daya manusia untuk mengelola Desa Wisata Banjaran tersebut. Kegiatan pengabdian masyarakat ini bertujuan  untuk membangun website Desa Wisata Banjaran sebagai media untuk memasarkan dan mempromosikan Desa Wisata Banjaran secara luas. Untuk mengoptimalkan implementasi website Desa Wisata, diberikan pelatihan untuk mengoperasikan website, diantaranya mengelola konten, mengelola sosial media yang terhubung dengan website sebagai pintu utama untuk mempromosikan Desa Wisata Banjaran. Capaian dari kegiatan ini diantaranya adalah pengembangan website Desa Wisata Banjaran, peningkatan softskill warga Desa Banjaran mengelola pemasaran Desa Wisata Banjaran melalui website dan sosial media.
Pemberdayaan Masyarakat melalui Pelatihan Pengolahan Susu Sapi Bernilai Tambah di Desa Nogosaren Nareswari, Andrea Keysa; Azkia, Naila Nur; Pramesti, Nadya Arum; Ningrum, Novita Kurnia
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 3 (2025): Vol 8, No 3 (2025): SEPTEMBER 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i3.3103

Abstract

Desa Nogosaren, Kecamatan Getasan, Kabupaten Semarang, memiliki potensi produksi susu sapi perah mencapai ±17.010 liter per hari. Namun, sebagian besar susu masih dijual secara mentah dengan harga rendah, sehingga nilai tambah bagi peternak kurang optimal dan sebagian susu berisiko terbuang. Program LALA (Pelatihan Pengolahan) dilaksanakan oleh tim PPK Ormawa HMTI untuk meningkatkan keterampilan masyarakat dalam mengolah susu sapi menjadi berbagai produk turunan bernilai jual tinggi. Metode pelaksanaannya meliputi sosialisasi, praktik langsung pembuatan produk makanan dasar (keju, yoghurt, dan butter) serta produk olahan lanjutan ( brownies, risoles keju, kroket keju, martabak manis, puding yoghurt, yoghurt buah, makaroni skotel, butterscotch, roti tawar softmilk), serta evaluasi melalui pre-test dan post-test. Hasil kegiatan menunjukkan peningkatan pengetahuan dan keterampilan peserta dalam mengolah susu sapi, yang ditunjukkan oleh antusiasme tinggi dan kemampuan membuat produk olahan sendiri dengan peralatan sederhana. Meningkatkan diversifikasi produk, mengurangi kemungkinan susu terbuang, dan membuka peluang bisnis baru adalah semua hasil nyata dari program ini. Oleh karena itu, pelatihan pengolahan susu sapi melalui Program LALA berhasil memberikan nilai tambah pada komoditas susu sapi di Desa Nogosaren.
Pemberdayaan Sosial Ekonomi Desa Nogosaren Melalui Pengembangan Produk Olahan Susu Sapi Taqi, Muhamad; Nabila, Isyeh Salma Bilqis; Saputra, Muhammad Alwi Nanda; Musyaffa, Muhammad Syihabuddin; Pramesti, Nadya Arum; Ningrum, Novita Kurnia
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 3 (2025): Vol 8, No 3 (2025): SEPTEMBER 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i3.3104

Abstract

Desa Nogosaren memiliki potensi peternakan sapi perah dengan 189 peternak yang menghasilkan 17.010 liter susu per hari, namun 5.100 liter terbuang akibat keterbatasan daya serap KUD dan kurangnya diversifikasi produk olahan. Penelitian bertujuan menganalisis kondisi produksi-pemasaran susu sapi, mengembangkan kapasitas masyarakat dalam diversifikasi produk olahan, dan menciptakan model pemberdayaan terintegrasi berbasis teknologi digital. Penelitian dilaksanakan Juli-Oktober 2025menggunakan pendekatan deskriptif kualitatif dengan purposive sampling melibatkan 36 peserta dari 12 kelompok wirausaha melalui wawancara mendalam, observasi partisipatif, dan dokumentasi komprehensif. Program GO-SMILE berhasil meningkatkan kapasitas diversifikasi produk keju, yogurt, dan butter dengan teknologi IoT, serta memperluas akses pasar melalui platform GO-SMILECommerce dan kolaborasi multipihak strategis. Model pemberdayaan terintegrasi berhasil menciptakan ekosistem ekonomi berkelanjutan, perlu diperkuat melalui sistem monitoring komprehensif dan ekspansi kemitraan strategis untuk keberlanjutan jangka panjang.
Implementasi Pelatihan Pemasaran Berbasis Digital sebagai Upaya Pemberdayaan Ekonomi Masyarakat Desa Nogosaren Setyawan, Aditya Rendy; Nugroho, Aulia Jilan; Qomariyah, Dania; Wicaksono, Aldo Anggara; Pramesti, Nadya Arum; Ningrum, Novita Kurnia
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 3 (2025): Vol 8, No 3 (2025): SEPTEMBER 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i3.3105

Abstract

Era digitalisasi menuntut transformasi pemasaran produk pertanian dan peternakan di wilayah pedesaan untuk meningkatkan daya saing ekonomi. Desa Nogosaren dengan potensi produksi susu sapi mencapai 17.010 liter per hari menghadapi permasalahan dalam pemasaran dan pengolahan produk yang optimal. Program GO-SMILE (Nogosaren Smart Milk Processing) diimplementasikan sebagai solusi pemberdayaan ekonomi masyarakat melalui pelatihan pemasaran berbasis digital. Metode partisipatif dengan pendekatan andragogi diterapkan melibatkan 36 peserta dari berbagai kategori masyarakat selama kurun waktu 2025-2027. Program terdiri dari dua komponen utama yaitu LALA (pelatihan pengolahan produk) dan TISA (pelatihan pemasaran digital). Hasil evaluasi menunjukkan peningkatan signifikan kemampuan digital marketing skills peserta dalam aspek manajemen konten dan penggunaan platform e-commerce. Dampak ekonomi yang positif tercapai dengan peningkatan pendapatan peserta dan perluasan jangkauan pasar dari lingkup desa ke tingkat regional. Program berhasil membentuk kader lokal dari karang taruna dan mencapai engagement rate 7,8% di Instagram. Implementasi pemasaran digital terbukti efektif meningkatkan pemberdayaan ekonomi masyarakat desa dan dapat direplikasi di desa-desa lain dengan karakteristik serupa.
Face Recognition Using MTCNN Face Detection, ResNetV1 Feature Embeddings, and SVM Classification Pratama, Ivan Putra; Ningrum, Novita Kurnia
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.v9i5.11016

Abstract

Face recognition has become an essential component of modern security and authentication systems, yet its effectiveness is often challenged by limited datasets, class imbalance, variations in facial poses, lighting conditions, and image resolutions. This study proposes a face recognition pipeline that integrates Multi-task Cascaded Convolutional Networks (MTCNN) for face detection, Residual Network V1 (ResNetV1) for feature extraction, and Support Vector Machine (SVM) for classification. Unlike previous works that rely on large-scale datasets and end-to-end deep learning models, this study emphasizes the effectiveness of the pipeline under constrained data conditions, using 856 images across 191 classes with highly imbalanced distribution. Experimental results show that MTCNN successfully detected 97.1% of faces, while ResNetV1 produced 512-dimensional embeddings that formed well-separated clusters validated by clustering metrics (Silhouette Score = 0.578, Davies-Bouldin Index = 0.566). The SVM classifier achieved 92.9% accuracy, with macro-average precision, recall, and F1-scores of 0.89, 0.92, and 0.89 respectively, significantly outperforming a baseline k-Nearest Neighbor (k-NN) model that only reached 63.9% accuracy. These findings highlight the novelty of this study: demonstrating that a lightweight yet robust pipeline can deliver reliable recognition performance even in small, imbalanced datasets, making it suitable for real-world scenarios where large-scale training data are not available.
Implementation of deep learning in the recommendation system for high school admissions in Semarang City Zaidan, Alfa Hikma; Ningrum, Novita Kurnia
Educenter : Jurnal Ilmiah Pendidikan Vol. 4 No. 2 (2025): Educenter: Jurnal Ilmiah Pendidikan
Publisher : ARKA INSTITUTE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55904/educenter.v4i2.1766

Abstract

The New Student Admission System for senior high schools in Semarang still faces challenges in achieving fair and proportional selection. The dominant zoning policy often ignores students' academic potential; therefore, a more comprehensive recommendation system is needed. This study proposes the development of a deep learning-based school recommendation system using a Multi-Layer Perceptron (MLP) architecture with a backpropagation algorithm. The dataset consists of 16 public senior high schools in Semarang, with the main variables including exam scores, age, school capacity, and distance from student residence calculated using the Euclidean distance method. The data is divided into a training set and a test set, with normalization applied to all numeric features. The training results show high accuracy. The system is able to generate school recommendation rankings that are visualized in tabular formats and interactive maps. Experimental results indicate that distance and school capacity contribute significantly to determining preference scores. Therefore, this study confirms that the deep learning approach is more adaptive than the rule-based linear method and can be an alternative solution to support a fairer and more transparent Student Admissions policy. For further research, it is recommended to develop the system by adding more diverse variables, real-time data integration, and implementing a more complex deep learning architecture to optimize the quality of recommendations.
Implementation of deep learning in the recommendation system for high school admissions in Semarang City Zaidan, Alfa Hikma; Ningrum, Novita Kurnia
Educenter : Jurnal Ilmiah Pendidikan Vol. 4 No. 2 (2025): Educenter: Jurnal Ilmiah Pendidikan
Publisher : ARKA INSTITUTE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55904/educenter.v4i2.1766

Abstract

The New Student Admission System for senior high schools in Semarang still faces challenges in achieving fair and proportional selection. The dominant zoning policy often ignores students' academic potential; therefore, a more comprehensive recommendation system is needed. This study proposes the development of a deep learning-based school recommendation system using a Multi-Layer Perceptron (MLP) architecture with a backpropagation algorithm. The dataset consists of 16 public senior high schools in Semarang, with the main variables including exam scores, age, school capacity, and distance from student residence calculated using the Euclidean distance method. The data is divided into a training set and a test set, with normalization applied to all numeric features. The training results show high accuracy. The system is able to generate school recommendation rankings that are visualized in tabular formats and interactive maps. Experimental results indicate that distance and school capacity contribute significantly to determining preference scores. Therefore, this study confirms that the deep learning approach is more adaptive than the rule-based linear method and can be an alternative solution to support a fairer and more transparent Student Admissions policy. For further research, it is recommended to develop the system by adding more diverse variables, real-time data integration, and implementing a more complex deep learning architecture to optimize the quality of recommendations.
Co-Authors Agustina, Feri Ahmad Syahreza Ajib Susanto Astuti, Yani Parti Azkia, Naila Nur Dwitama, Reiza Hersa Egia Rosi Subhiyakto, Egia Rosi Erlin Dolphina Erna Zuni Astuti Ibnu Utomo WM Ivan Putra Pratama Karis Widyatmoko Kurniawan, Defri Kusumawati, Yupie Liya Umaroh, Liya Muhammad Naufal, Muhammad Mulyono, Ibnu Utomo Wahyu Musyaffa, Muhammad Syihabuddin Nabila, Isyeh Salma Bilqis Nareswari, Andrea Keysa Nugroho, Aulia Jilan Pramesti, Nadya Arum Qomariyah, Dania Saputra, Muhammad Alwi Nanda Sasmita, Tomy Elen Sembiring, Rinawati Septian Enggar Sukmana, Septian Enggar Setyawan, Aditya Rendy SUDARYANTO SUDARYANTO Sukamto, Titien Suhartini Sunyaruri, Wisesa Sat Syahrazy, Muhammad Anjas Syahreza, Ahmad Tangkas Surya Wibawa Taqi, Muhamad Trisnapradika, Gustina Alfa Utomo, Danang Wahyu Wicaksono, Aldo Anggara Zahrotul Umami, Zahrotul Zaidan, Alfa Hikma