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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Innovation in Research of Informatics (INNOVATICS) Jurnal Sains, Teknologi dan Rekayasa (SAINTESA) Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Jurnal Penelitian dan Pengabdian Masyarakat
ALAM
Universitas Cipasung Tasikmalaya
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Mechanical Engineering Social Sciences Other

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Rancang Bangun Sistem Prediksi Varietas Padi Yang Cocok Dengan Lahan Menggunakan Metode Data Mining Algoritma C4.5 Dewanto Rosian Adhy; Alam
Jurnal Ilmiah Sains, Teknologi dan Rekayasa Vol 1 No 1 (2021): Oktober 2021
Publisher : P3M STT YBSI Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (791.506 KB)

Abstract

Kebutuhan pangan di Indonesia terus meningkat setiap tahunnya mengikuti pesatnya pertumbuhan penduduk. Untuk mengatasi masalah tersebut, produksi beras juga harus ditingkatkan. Namun, saat ini produk pertanian terkadang tidak menentu karena perubahan cuaca yang tidak terduga. Pemilihan varietas padi yang akan ditanam juga harus lebih selektif untuk menghindari resiko gagal panen dan penurunan hasil. Penelitian ini bertujuan untuk membantu petani di Kabupaten Tasikmalaya dalam menentukan varietas padi yang cocok dengan kondisi tanah dan cuaca. Penelitian ini menggunakan metode penelitian kuantitatif. Algoritma Data Mining yang digunakan dalam penelitian ini adalah algoritma Data Mining Decission Tree (C4.5).
Comparison Of The C.45 And Naive Bayes Algorithms To Predict Diabetes Alam, Alam; Alana, Divi Adiffia Freza; Juliane, Christina
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12998

Abstract

Diabetes mellitus is an urgent global health problem and has a major impact on people around the world. This disease is characterized by high levels of sugar (glucose) in the blood due to disturbances in the production or use of the hormone insulin by the body. This study aims to carry out accurate early detection of diabetics so that they can be treated as soon as possible to reduce the risk of death and to compare the two algorithms that have the best level of accuracy. The algorithms used in this study are the C4.5 and Naïve Bayes Decision Tree Algorithms. The results of the experiments carried out in this study the Decision Tree Algorithm C4.5 and Naïve Bayes can be used in modeling the early detection of diabetes. The highest average accuracy results were obtained at 90.835% using the Decision Tree C4.5 Algorithm. As for the Naïve Bayes Algorithm, an average accuracy rate of 90.745% is obtained. The pruning process was carried out using the Decision Tree Algorithm C4.5, the accuracy performance increased to 91.30%. There were 18 patterns or rules for the early detection of diabetics from the built model. The determination of attributes, the number of attribute dimensions, and the number of samples greatly affect the performance of the model built.
PEMANFAATAN DIGITALISASI DALAM BERWIRAUSAHA DI ERA INDUSTRI 4.0 BAGI USAHA MIKRO KECIL DAN MENENGAH (UMKM) Febriani SM, N Nelis; Sudiarti, Sri; Ghurroh Setyoningrum, Nuk; Aini Syifa, Raisa Hilia; Ardhiansyah; Alam
Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Vol. 3 No. 1 (2025): Februari
Publisher : CV. Alina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jpki2.v3i1.1788

Abstract

Era Industri 4.0 membawa transformasi besar dalam berbagai sektor, termasuk Usaha Mikro, Kecil, dan Menengah (UMKM). Digitalisasi menjadi peluang strategis bagi UMKM untuk meningkatkan daya saing di tengah perubahan teknologi yang pesat. Pemanfaatan teknologi digital, seperti e-commerce, media sosial, dan platform berbasis aplikasi, memungkinkan UMKM memperluas jangkauan pasar, meningkatkan efisiensi operasional, serta menciptakan inovasi produk dan layanan. Tujuan dilaksanakan kegiatan pengabdian ini untuk meningkatkan kapasitas digitalisasi pada Usaha Mikro, Kecil, dan Menengah (UMKM) dalam menghadapi tantangan dan peluang di era Industri 4.0. Melalui sosialisasi dan pendampingan, kegiatan ini membantu pelaku UMKM memahami dan memanfaatkan teknologi digital, seperti platform e-commerce, media sosial, dan aplikasi manajemen bisnis, untuk mendukung operasional dan pemasaran usaha mereka. Kegiatan ini dilaksanakan dengan sosialisasi interaktif, diskusi peserta, dan praktik langsung yang disesuaikan dengan kebutuhan spesifik UMKM. Hasilnya menunjukkan peningkatan pemahaman peserta tentang pentingnya digitalisasi, kemampuan menggunakan teknologi digital, serta strategi pemasaran berbasis digital. Dampak langsung yang dirasakan adalah meningkatnya jangkauan pasar, efisiensi operasional, dan daya saing UMKM di tengah perubahan ekonomi yang semakin terhubung secara digital. Kegiatan ini diharapkan dapat menjadi model pemberdayaan UMKM yang relevan di era Industri 4.0.
The Impact of Linguistic Features on Emotion Detection in Social Media Texts Setyoningrum, Nuk Ghurroh; Febriani SM, Neng Nelis; Alam, Alam; Nurdin, Arif Muhamad; Nursamsi, Dede Rizal; Lodana, Mae B
Innovation in Research of Informatics (Innovatics) Vol 7, No 1 (2025): March 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i1.13221

Abstract

Emotions are an important aspect of human life, and scientific theories on emotions have been widely developed in various research fields such as philosophy, psychology, and neuroscience. In human-computer interaction, understanding emotions is also very important. Detecting emotions not only enables better decision-making, but is also useful in various contexts such as business, politics, and mental health. The focus on identifying emotions in text arises because emotions are often implied without explicit words. Through the analysis of grammar and sentence structure, text mining techniques enable the extraction of sentiments and emotions. Detecting and identifying emotions in text is important because it can be applied in a variety of fields, including decision-making, prediction of human emotions, product assessment, analysis of political support, and identification of depression. Text as textual data is an important source of information due to its ability to convey human emotions. In this research, emotion detection uses the Naïve Bayes method, with attribute weighting to improve accuracy using count vector. This classification approach allows grouping text into six emotion categories: happy, sad, fear, love, shock, and anger. The Naïve Bayes method was chosen for its reliability in classifying data based on conditional probabilities. Thus, this research provides a deeper understanding of understanding and managing emotions in the context of social media. The data classification results yield precision, recall, F1-Measure, and accuracy values.
Pendampingan Pengembangan Ekonomi Kreatif Berbasis Potensi Lokal di Desa Wisata Sudiarti, Sri; Rusliana, Nanang; Setyoningrum, Nuk Gurroh; Alam, Alam; Syfa, Raisa Hillia Aini
Jurnal Penelitian dan Pengabdian Masyarakat Vol. 3 No. 3 (2025): August 2025
Publisher : Yayasan Pondok Pesantren Sunan Bonang Tuban

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61231/jp2m.v3i3.379

Abstract

This community mentoring activity aims to identify local potential that can be developed into creative economic products and formulate development strategies appropriate to the characteristics of tourist villages. The mentoring approach uses socialization and talk shows. The mentoring results indicate that creative economy development based on local potential, such as arts and crafts, traditional culinary arts, local culture, and ecotourism, can increase community income and strengthen local identity. Recommended development strategies include increasing human resource capacity, collaborating with various stakeholders, utilizing digital technology for promotion, and strengthening village institutions. In conclusion, developing a creative economy based on local potential requires a participatory, sustainable, and adaptive approach to the dynamics of the tourism market
Data Augmentation Strategies on Spectrogram Features for Infant Cry Classification Using Convolutional Neural Networks Alam, Alam; Setyoningrum, Nuk Ghurroh; Maududy, Robby; Damayanti, Dea Dewi; Rahmawati, Hilmi
Innovation in Research of Informatics (Innovatics) Vol 7, No 2 (2025): September 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i2.16823

Abstract

Infant cry classification is an important task to support parents and healthcare professionals in understanding infants’ needs, yet the challenge of limited and imbalanced datasets often reduces model accuracy and generalization. This study proposes the application of diverse audio data augmentation strategies including time stretching, time shifting, pitch scaling, and polarity inversion combined with spectrogram representation to enhance Convolutional Neural Network (CNN) performance in classifying infant cries. The dataset from the Donate-a-Cry Corpus was expanded from 457 to 6,855 samples through augmentation, improving class balance and variability. Experimental results show that CNN accuracy increased from 85% before augmentation to 99.85% after augmentation, with precision, recall, and F1-score reaching near-perfect values across all categories. The confusion matrix further confirms robust classification with minimal misclassifications. These findings demonstrate that data augmentation is crucial to overcoming dataset limitations, enriching acoustic feature diversity, and reducing model bias, while offering practical implications for the development of accurate, reliable, and real-world applicable infant cry detection systems.
Co-Authors Adhy, Dewanto Rosian Aini Syifa, Raisa Hilia Alana, Divi Adiffia Freza Ardhiansyah Christina Juliane, Christina Damayanti, Dea Dewi Ghurroh Setyoningrum, Nuk Lodana, Mae B Maududy, Robby N. Nelis Febriani SM Nuk Ghurroh Setyoningrum Nurdin, Arif Muhamad Nursamsi, Dede Rizal Rahmawati, Hilmi Rusliana, Nanang Setyoningrum, Nuk Gurroh sri sudiarti Syfa, Raisa Hillia Aini