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All Journal Teknika Proceeding International Conference on Information Technology and Business JUITA : Jurnal Informatika Jurnal CoreIT Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Abdi Insani Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) TIERS Information Technology Journal Proceeding Applied Business and Engineering Conference Jurnal Ilmiah ESAI
Zuriati, Zuriati
Politeknik Negeri Lampung
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The Implementation of Internet of Things (IOT) for Aquaponic Cultivation Zuriati, Zuriati; Widyawati, Dewi Kania; Dulbari, Dulbari; Zarnelly, Zarnelly
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 2 (2024): December 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v10i2.29541

Abstract

Aquaponic is a plant cultivation technique that is widely used by farmers and today’s communities due to its efficiency and ability to increase the agricultural productivity. The aquaponic cultivation in general still uses simple systems, such as manually feeding the fish by spreading the feed at predetermined times, monitoring water pH using a pH meter and monitoring water height or level through measurements, requiring farmers to spend time and special labor to care for and maintain plants and fish. Therefore, a solution is needed in the form of a system that can monitor and control plants and fish conditions automatically and continuously for 24 hours. The system should have the ability to control and monitor feeding activities, water pH, water and environmental temperature, water level and environmental humidity. The system in question is the internet of things (IoT) system that can be used as a tool for automatic control and monitoring through an application. The IoT system consists of several sensors that are connected to a microcontroller which can measure water pH, temperature, water level and environmental humidity. The data obtained by the sensor will be sent to a server via Wi-Fi protocol and stored in a database. The system is equipped with a web application that can be accessed through a computer device. The application provides a visual display of data: time, water pH, temperature, water level and environmental humidity, making it easier for farmers to monitor aquaponic conditions from a distance without having to come to the land. Through the implementation of IoT in aquaponic cultivation, farmers can increase efficiency and agricultural productivity by reducing the time, labor and costs required for control and monitoring.
PENERAPAN SOP PENGOLAHAN KOPI GULA AREN, KOPI DURIAN, DAN KOPI AVOCADO INSTAN DI UMKM KOPI LAMPUNG NUSANTARA Analianasari, Analianasari; Subiantoro, Eko; Zuriati, Zuriati; Afifah, Dian Ayu
Jurnal Abdi Insani Vol 12 No 9 (2025): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v12i9.2652

Abstract

Semakin banyak kedai kopi yang berkembang di Bandar Lampung membuat bisnis kopi menjadi lebih baik. Hal ini menyebabkan pasar kopi menjadi semakin bersaing, dan produsen kopi membutuhkan inovasi dan diversifikasi dalam olahan kopi mereka. Inovasi dan diversifikasi produk dalam tahapan produksinya membutuhkan Prosedur Operasional Standar (SOP) yang jelas dan konsisten. Tujuan Pemberdayaan ini adalah untuk mendorong mitra menghadirkan produk yang berbeda dari produk yang ada selama ini.  Kegiatan ini mengembangkan kopi olahan rasa durian, avocado, dan kopi gula aren sebagai diversifikasi produk dengan menerapkan SOP Produksi pada varian kopi sebagai keunggulan kompetitif UMKM Kopi Lampung Nusantara.  Pelaksanaan metode pengabdian kepada masyarakat dilaksanakan pada UMKM Lampung Nusantara Kopi dilakukan tiga tahapan,s yaitu (1) observasi secara langsung kepada pelaku usaha UMKM terhadap permasalahan yang dihadapi dengan metode wawancara; (2) Sosialisasi dengan tema diversifikasi olahan kopi (gula aren, kopi durian, dan avocado) untuk meningkatkan keunggulan kompetitip produk UMKM; (3) Pendampingan dan pelaksanaan pelatihan pengolahan diversifikasi kopi untuk varian kopi gula aren, kopi durian dan kopi avocado. sesuai dengan SOP.  Hasil pelatihan memberikan peningkatan pengetahuan dan keterampilan UMKM Kopi Lampung Nusantara dengan varian rasa durian, avocado, dan gula aren.  Penerapan SOP Pengolahan diversifikasi tiga olahan kopi diharapkan dapat dilakukan secara konsisten untuk menjaga kualitas produk UMKM sehingga dapat bersaing dengan produk lain.
Enhancing Chronic Kidney Disease Classification Using Decision Tree And Bootstrap Aggregating: Uci Dataset Study With Improved Accuracy And Auc-Roc Zuriati, Zuriati; Meilantika, Dian; Arpan, Atika; Permata, Rizka; Sriyanto, Sriyanto; Mas'ud, Mohd. Zaki
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5271

Abstract

Chronic Kidney Disease (CKD) is a progressive medical disorder that requires timely and precise identification to avoid permanent impairment of kidney function. However, Decision Tree models, although widely used in clinical applications due to their transparency, ease of implementation, and ability to handle both categorical and numerical data, are prone to overfitting and instability when applied to small or imbalanced datasets. The purpose of this study is to optimize CKD classification by integrating Bootstrap Aggregating (Bagging) with Decision Tree to enhance accuracy and robustness. The methodology involves testing two model variants a standalone Decision Tree and a Bagging-supported Decision Tree using 10-fold cross-validation and evaluating performance with accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Findings reveal that Bagging enhances model accuracy from 0.980 to 0.987, raises precision from 0.976 to 1.000, and improves recall from 0.954 to 0.954, and increases F1-score from 0.965 to 0.976. These results demonstrate that Bagging significantly improves the reliability and generalizability of Decision Tree classifiers, making them more effective for CKD prediction.
Pelatihan Pemasaran Digital Pada UMKM Dapur Omah Cinta: Langkah Menuju Transformasi Digital Zuriati, Zuriati; Subyantoro, Eko; Asrowardi, Imam; Dwi Putra, Septafiansyah
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 5 No. 4 (2024): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN) Edisi September - Desembe
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v5i4.4843

Abstract

Digitalisasi UMKM merupakan langkah strategis untuk meningkatkan daya saing di tengah tantangan globalisasi dan persaingan pasar yang semakin ketat. Kegiatan Pengabdian Kepada Masyarakat (PKM) Inovokasi ini bertujuan untuk mempercepat transformasi digital UMKM Dapur Oma Cinta melalui pelatihan pemasaran digital yang terstruktur. Permasalahan utama yang dihadapi UMKM ini adalah keterbatasan akses pasar dan kurangnya optimalisasi teknologi digital dalam pemasaran produk. Metode yang digunakan meliputi tahapan persiapan, pelaksanaan, monitoring, dan evaluasi. Pada tahap persiapan, tim menyusun materi pelatihan dan menyiapkan sarana pendukung. Tahap pelaksanaan dilakukan melalui sesi pelatihan interaktif dan pendampingan langsung yang mencakup pengelolaan website e-commerce, marketplace, media sosial, dan strategi pembuatan konten digital. Evaluasi keberhasilan dilakukan melalui pre-test dan post-test untuk mengukur peningkatan kemampuan peserta. Hasil menunjukkan peningkatan signifikan dalam pemahaman dan keterampilan peserta terkait pemasaran digital. Rata-rata peningkatan skor post-test peserta mencapai 66,84%, dengan peningkatan terbesar pada peserta dengan pengetahuan awal yang rendah. Temuan penting menunjukkan bahwa pelatihan ini berhasil membantu UMKM memperluas jangkauan pasar, meningkatkan efektivitas promosi, dan memperkuat daya saing di pasar lokal. Kesimpulannya, program ini tidak hanya mendukung transformasi digital UMKM Dapur Oma Cinta tetapi juga memberikan model pelatihan yang dapat direplikasi untuk UMKM lain di Indonesia. Transformasi digital yang dicapai melalui kegiatan ini berpotensi besar untuk meningkatkan pendapatan dan keberlanjutan usaha mitra dalam jangka panjang.
PENINGKATAN KOMPETENSI DIGITAL GURU MELALUI PELATIHAN KODING DAN KECERDASAN ARTIFISIAL BERBASIS DEEP LEARNING DI SMAN 2 KALIANDA Kania Widyawati, Dewi; Arifin, Oki; Maulini, Rima; Zuriati, Zuriati; Sahlinal, Dwirgo; Pratama, Yoga; Ari Wijaya Saputra, I Komang; Bulan Nayla, Amanda
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 8, No 11 (2025): MARTABE : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v8i11.4100-4108

Abstract

Implementasi Kurikulum Merdeka di SMAN 2 Kalianda memerlukan pendekatan inovatif seperti pembelajaran mendalam (Deep Learning) yang berfokus pada tiga pilar yaitu pembelajaran sadar (Mindful Learning) menyesuaikan materi dengan kebutuhan siswa dan mendorong fokus penuh,  pembelajaran bermakna (Meaningful Learning) melatih berpikir kritis dan mengaitkan konsep dengan kehidupan nyata, serta pembelajaran menyenangkan (Joyful Learning) menciptakan pengalaman belajar yang interaktif dan memotivasi. Namun, pengintegrasian teknologi seperti pemrograman Python dan kecerdasan artifisial masih terhambat oleh keterbatasan kompetensi guru. Program pengabdian ini bertujuan untuk melatih guru dalam penguasaan koding, kecerdasan artifisial, membimbing pendidik merancang modul berbasis proyek, serta mengembangkan bahan ajar digital yang sesuai dengan Kurikulum Merdeka. Pengabdian ini dilaksanakan melalui pendekatan partisipatif kolaboratif yang melibatkan mitra secara aktif. Metode pelaksanaan melalui lima tahapan yaitu sosialisasi, pelatihan, penerapan teknologi, pendampingan dan evaluasi, serta keberlanjutan program. Peserta dilatih untuk memahami algoritma pemrograman, pemrograman Python, pembuatan model AI sederhana, dan bahan ajar digital melalui Learning Management System (LMS) sekolah. Pelatihan dan evaluasi berbasis pre-test dan post-test menunjukkan hasil peningkatan signifikan dalam pemahaman peserta dengan rata-rata nilai sebesar 64,88 menjadi 96,63 dan N-gain score sebesar 91,36%. Hal ini menunjukkan efektivitas program dalam meningkatkan pengetahuan peserta tentang koding dan kecerdasan artifisial.
Comparative Analysis Of Machine Learning Algorithms For Dengue Fever Prediction Based On Clinical And Laboratory Features Sriyanto, Sriyanto; Aziz, RZ Abdul; Rahayu, Dewi Agushinta; Zuriati, Zuriati; Abdollah, Mohd Faizal; Irianto, Irianto
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5309

Abstract

Dengue fever (DF) remains a global health problem requiring accurate early detection to prevent severe complications. This study applies machine learning (ML) algorithms to clinical and laboratory data for improving diagnostic accuracy. Six classifiers were compared: Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Naïve Bayes (NB), Neural Network (NN), and Support Vector Machine (SVM). The dataset consists of 1,003 patient records with nine feature columns, of which 989 were used after preprocessing. Class distribution was imbalanced, with 67.6% positive and 32.4% negative cases. Model performance was evaluated using 10-fold cross-validation based on accuracy, precision, recall, F1-score, confusion matrix, and ROC curve analysis. The results indicate that DT achieved the highest performance with 99.4% accuracy, 99.4% precision, 99.7% recall, and 99.6% F1-score, slightly outperforming NN. KNN, LR, and SVM produced comparable results, while NB showed substantially lower accuracy (44.3%) and limited discriminatory power. ROC analysis confirmed these findings, with DT, NN, SVM, and LR achieving AUC values between 0.992 and 0.999, whereas NB performed poorly. These findings highlight the strong potential of ML algorithms, particularly DT, to support medical decision systems, strengthen informatics-based decision support applications, and enhance the accuracy and speed of dengue diagnosis in clinical practice.
Hybrid Machine Learning Approach for Nutrient Deficiency Detection in Lettuce Zuriati, Zuriati; Widyawati, Dewi Kania; Arifin, Oki; Saputra, Kurniawan; Sriyanto, Sriyanto; Ahmad, Asmala
TIERS Information Technology Journal Vol. 6 No. 2 (2025)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v6i2.7143

Abstract

Early detection of nutrient deficiencies in lettuce is essential for precision agriculture. However, this task remains challenging due to limited data availability and class imbalance, which reduce model sensitivity toward minority classes and hinder generalization. This study introduces a hybrid machine learning approach integrating SMOTE, Optuna, and SVM to enhance the accuracy of nutrient deficiency classification using digital leaf image analysis. The dataset, obtained from Kaggle, includes four categories: Nitrogen Deficiency (-N), Phosphorus Deficiency (-P), Potassium Deficiency (-K), and Fully Nutritional (FN). Image features were extracted using MobileNetV2 pretrained on ImageNet and classified with a Support Vector Machine. Three scenarios were tested: (1) SVM before SMOTE, (2) SVM after SMOTE, and (3) Optuna-SVM after SMOTE, evaluated using accuracy, precision, recall, and f1-score. The hybrid model achieved the best performance with accuracy 0.929, precision 0.946, recall 0.835, and f1-score 0.869, outperforming the other scenarios. This hybrid framework effectively addressed class imbalance and improved classification margin stability through adaptive hyperparameter tuning using the Tree Structured Parzen Estimator within Optuna. The novelty of this study lies in combining MobileNetV2 based feature extraction with SMOTE and Optuna-SVM for small agricultural datasets. The proposed approach offers an efficient, accurate, and practical solution for automated nutrient deficiency diagnosis and contributes to the development of AI-driven smart agriculture systems.
Evaluation of CNN Architectures for Kidney Stone Classification in Ultrasound Image Zuriati, Zuriati; Sriyanto, Sriyanto; Supriyatna, Agiska Ria; Qomariyah, Nurul; Afifah, Dian Ayu; Zarnelly, Zarnelly
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

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

Abstract

Kidney stone diagnosis requires fast and reliable evaluation, yet ultrasound interpretation still largely depends on clinical expertise. This study evaluates four Convolutional Neural Network (CNN) architectures, VGG16, ResNet50, MobileNetV2, and EfficientNetB0 for classifying kidney ultrasound images into Normal and Stone categories. Using a public dataset of 9,416 images, the models were assessed in terms of predictive performance and computational efficiency. MobileNetV2 achieved perfect classification performance, recording 100% accuracy, precision, recall, and F1-score, while maintaining the smallest parameter size (≈3.6M) and fastest training time (~44 s/epoch). VGG16 and ResNet50 also delivered near perfect accuracy (99.79% and 99.89%) with full recall for Stone cases. In contrast, EfficientNetB0 failed to generalize, yielding only 51.62% accuracy due to severe misclassification of Normal images. These results demonstrate that MobileNetV2 provides the most reliable and efficient solution for ultrasound based kidney stone classification, highlighting its strong potential for practical clinical deployment.
Co-Authors Abdollah, Mohd Faizal Afifah, Dian Ayu Ahmad, Asmala Analianasari Analianasari Ari Wijaya Saputra, I Komang Arifin, Oki Arpan, Atika Asrowardi, Imam Aziz, RZ. Abdul Bulan Nayla, Amanda Dewi Agushinta R. Dewi Kania Widyawati Dewi Kania Widyawati Dewi Kania Widyawati Dulbari, Dulbari Dwirgo Sahlinal Dwirgo Sahlinal, Dwirgo Irianto Irianto Kania Widyawati, Dewi Kurniawan Saputra Mas'ud, Mohd. Zaki Maulini, Rima meilantika, Dian Nurul Qomariyah Nurul Qomariyah Oktaf Rina Permata, Rizka Pratiwi, Dilia Ananda Putra, Septafiansyah Dwi Rima Maulini Sriyanto, Sriyanto Subiantoro, Eko Subyantoro, Eko Supriyatna, Agiska Ria Sylvia, Sylvia Widyawati, Dewi Kania Yadi, Asrol Yazid Nabil I, Hauzan Yoga Pratama Zarnelly Zarnelly