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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of Economics, Business, & Accountancy Ventura Journal of Information Systems Engineering and Business Intelligence Tech-E Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputasi JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Tekno Kompak Building of Informatics, Technology and Science Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Sisfotek Global Journal of Computer System and Informatics (JoSYC) Community Development Journal: Jurnal Pengabdian Masyarakat IJPD (International Journal Of Public Devotion) Jurnal Teknologi dan Sistem Tertanam Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Data Mining dan Sistem Informasi Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Sisfotek Global COMMENT: Journal of Community Empowerment Journal of Engineering and Information Technology for Community Service Jurnal Ilmiah Edutic : Pendidikan dan Informatika Jurnal Pengabdian kepada Masyarakat (Nadimas) Jurnal Media Borneo Jurnal Informatika: Jurnal Pengembangan IT Jurnal Media Celebes Journal of Artificial Intelligence and Technology Information Journal of Information Technology, Software Engineering and Computer Science The Indonesian Journal of Computer Science Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Komputasi Journal of Soft Computing Exploration
Alita, Debby
Universitas Teknokrat Indonesia
Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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SENTIMEN ANALISIS PUBLIK TERHADAP KEBIJAKAN LOCKDOWN PEMERINTAH JAKARTA MENGGUNAKAN ALGORITMA SVM Isnain, Auliya Rahman; Sakti, Adam Indra; Alita, Debby; Marga, Nurman Satya
Jurnal Data Mining dan Sistem Informasi Vol 2, No 1 (2021): Februari 2021
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jdmsi.v2i1.1021

Abstract

Media sosial menjadikan masyarakat mengalami pergeseran perilaku baik budaya, etika dan norma yang ada, sehingga mereka dapat mengeluarkan opini-opini yang mereka miliki. Opini merupakan suatu pendapat dari pemikiran masayarakat mengenai suatu permasalahan yang sedang terjadi, saat ini Indonesia sedang dihadapkan oleh masalah mengenai virus Covid-19 yang memakan begitu banyak korban jiwa sehingga masyarakat mengeluarkan opini mereka mengenai virus tersebut dan kebijakan yang dilakukan pemerintah menghadapi virus tersebut.Penelitian ini bertujuan untuk mengetahui bagaimana sentiment publik terhadap kebijakan yang akan dilakukan pemerintah mengenai kebijakan lockdown ataupun pembatasan sosial berskala besar menggunakan metode Support Vector Machine denga ekstraksi fitur tf-idf  dengan pengujian yang nantinya akan dilihat bagaimana nilai accuracy, precision, Recall dan F1-Score.Penggunaan metode Support Vector Machine dan ekstraksi fitur dengan tf-idf yang membagi kelas menjadi sentiment positif 68,75% dan negative 31,25% menghasilkan nilai accuracy sebesar 74%, precision sebesar 75%, recall sebesar 92% dan F1-Score sebesar 83%.
ANALISIS SENTIMEN MASYARAKAT TERHADAP KASUS JUDI ONLINE MENGGUNAKAN DATA DARI MEDIA SOSIAL X PENDEKATAN NAIVE BAYES DAN SVM M Febrian As Shidiq; Debby Alita
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 1 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i1.3624

Abstract

Research conducted by analyzing public sentiment related to online gambling cases using datasets from x social media using the naïve bayes method approach and support vector machine (SVM). The analysis phase starts with data gathering or crawling, followed by data labeling, data preprocessing, and ultimately method categorization. The dataset comprises 2,866 tweets, with 1,436 classified as positive (50.12%) and 1,429 as negative (49.88%). The data before to the classification process is partitioned into training data and testing data, including 70% training data and 30% testing data. The analysis with the SVM approach yielded a classification accuracy of 83%, whereas the naïve Bayes method achieved just 79%. Upon completion of the method classification process, the subsequent phase involves visualization and assessment. During the visualization step, bar plots, word clouds, and word frequencies derived from sentiment analysis calculations are shown, alongside a visualization of words from the dataset. The investigation indicates that the SVM approach outperforms Naive Bayes in sentiment classification. The benefit of SVM resides in its capability to manage data with elevated limits and accuracy, enhancing its efficiency in discerning positive and negative thoughts. The findings of this study demonstrate that SVM is better appropriate for data exhibiting complicated distributions, whereas the Naive Bayes approach yields suboptimal results. Thus, SVM can be proposed as a more appropriate and reliable approach for similar sentiment analysis in the future.
Penerapan Metode Waterfall dalam Pengembangan Sistem Persediaan Ayam Boiler Berbasis Web Mobile Anan Krisna; Debby Alita; Slamet Samsugi
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 3 No. 2 (2025): Volume 3 Number 2 June 2025
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v3i2.204

Abstract

Berkah Unggas Farm Jati Agung merupakan salah satu bentuk usaha perseorangan yang bergerak di bisnis jual beli ayam boiler. Berdasarkan wawancara terhadap pemilik Berkah Unggas Farm bahwa proses pengolahan data persediaan ayam yaitu pengolahan dan pencatatan yang masih menggunakan cara manual (perekapan menggunakan buku besar) sehingga ketidakakuratan data, ada risiko kesalahan manusia yang tinggi, seperti kesalahan penulisan data, seperti stok yang tercatat salah atau kesalahan dalam pencatatan transaksi. Ketidakakuratan data dapat menyebabkan keputusan yang salah dan mengganggu efisiensi operasional masuk dan keluar ayam boiler. Berdasarkan masalah tersebut dibuatlah sistem pengelolaan ayam boiler berbasis mobile web. Sistem tersebut, dirancang dan dibangun menggunakan metode pengembangan sistem waterfall, serta sistem diuji dengan menggunakan ISO 25010. Sistem yang telah dibangun dapat membantu proses pengolahan data persediaan ayam yang menghasilkan data yang lebih akurat, meminimalisir adanya risiko kesalahan manusia khususnya dalam pengolahan data, seperti kesalahan penulisan data, seperti stok yang tercatat salah atau kesalahan dalam pencatatan transaksi, mempermudah pengelolaan persediaan ayam boiler lebih cepat dari sistem sebelumnya. Hasil pengujian kelayakan sistem menggunakan ISO 25010 mendapatkan hasil sebesar 97%. Secara skala likert bahwa kelayakan sistem informasi yang telah dibuat memiliki keberhasilan Sangat Baik, sehingga sistem layak diterapkan dan diimplementasikan di Berkah Unggas Farm Jati Agung.
Pendeteksian Sarkasme pada Proses Analisis Sentimen Menggunakan Random Forest Classifier Debby Alita; Auliya Rahman Isnain
Jurnal Komputasi Vol. 8 No. 2 (2020)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v8i2.2615

Abstract

Kalimat sindiran atau sarkasme masih sering digunakan oleh kalangan publik untuk mengungkapkan maksud isi hati dan pikiran baik itu yang disampaikan secara langsng maupun tidak langsung. Sarkasme dilakukan untuk menyindir dan menyakiti hati seseorang dengan menggunakan bahasa atau kata yang didalamnya mengandung kata positif tetapi maknanya negatif sehingga sering sekali terjadi opini salah diklasifikasikan. Penelitian ini melakukan kombinasi antara proses sentimen analisis dengan deteksi sarkasme untuk pengklasifikasian opini yang terdapat pada Twitter. Proses analisis sentimen dilakukan dengan tahapan preprocessing dan ekstraksi fitur dan diklasifikan dengan menggunakan metode Support Vector Machine dilanjutkan dengan proses pendeteksian sarkasme yang dilakukan tahapan ekstraksi fitur dengan 4 set fitur yaitu sentiment related, punctuation-relate, lexical and syntactic, dan pattern-relate dan diklasifikasikan dengan menggunakan metode Random Forest Classifier. Hasil penelitian ini didapatkan peningkatan nilai rata-rata akurasi sebesar 16,61 %, nilai presisi sebesar 5,45 %, nilai recall sebesar 9,64% dan kenaikan nilai F1score sebesar 11,27% dengan jumlah data sebanyak 2.027 dengan rincian data dengan label positif berjumlah 1023, data dengan label negatif berjumlah 587 dan data dengan label netral berjumlah 462. Data sarkasme didapatkan dari tweet dengan label positif yang kemudian diberikan label sarkasme atau tidak sarkasme dan didapat hasil label dengan jumlah keseluruhan berlabel sarkasme berjumlah 354 dan tidak sarkasme berjumlah 669.
A deep learning-based leaf aphid detection approach using YOLOv8 Styawati, Styawati; Sulistiani, Heni; Puspaningrum, Ajeng Savitri; Alita, Debby; Samsugi, S.; Putri, Vanisa Adellia
Journal of Soft Computing Exploration Vol. 7 No. 1 (2026): March 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i1.9

Abstract

Aphids pose a serious threat to agricultural productivity due to their rapid reproduction and their role as plant virus vectors. Early manual detection is difficult due to the pests' microscopic size and tendency to hide under leaves. This study aims to develop an accurate and real-time aphid monitoring system using the YOLOv8 algorithm. The model was trained using four epoch scenarios (30, 50, 100, and 200) to identify the best configuration to address the challenges of small, overlapping objects and varying leaf backgrounds. The results showed that increasing the number of epochs positively correlated with model performance, with the 200-epoch scenario providing the most optimal results with 91.5% accuracy, 0.87 recall, 0.89 F1-score, and 0.915 mAP50. The model was then integrated into a smart monitoring dashboard that synchronizes visual detection results with IoT sensor data (temperature, humidity, and nutrients) in real time. This system not only validates the reliability of YOLOv8 under field conditions, but also provides an effective early warning system to support rapid decision-making in crop protection management.
A Hybrid AI–SEMPLS Model for Digital Visualization Acceptance in Blue Tourism: Evidence from Lampung Province Alita, Debby; Nisa, Khoirin; Styawati; Amelia, Dina
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.2909

Abstract

Blue tourism destinations often lack advanced digital tools capable of providing real-time, AI-driven visualization and user-centered information services. This study addresses this gap by developing JELAMBU, an AI-enabled digital visualization platform, and by evaluating user acceptance through a hybrid SEMPLS models. The research aims to: (i) design and implement an AI-based system that combines chatbot interaction, realtime sentiment analytics, and digital visualization; and (ii) examine the determinants of tourists’ intention to adopt AI-enabled e-tourism technologies. A structured questionnaire was administered to 467 visitors of destinations, and 16 hypotheses were tested. The results show that platform design, facilitating conditions, AI technology, perceived ease of use, perceived usefulness, social influence, service quality, trust, and risk perception significantly shape intention to use, whereas information quality, perceived benefits, and performance expectancy do not show significant effects. The model demonstrates substantial predictive power (R² = 0.703), strong effect sizes (f² > 0.225), and acceptable fit (SRMR = 0.084). These findings highlight the pivotal role of design and system conditions in AI-driven tourism platforms and provide practical guidance for developers and policymakers in strengthening digital visualization, personalization features, and sustainable blue tourism management. Future studies may extend this framework to multi-regional settings or longitudinal adoption scenarios.
Co-Authors ., Rusliyawati Ade Dwi Putra Ade Setiawan alifa, Chairunnisa fadia Anan Krisna Andi Kurniawan Andi Nurkholis Anggraini, Jenny Aris Munandar Asmawati Asmawati Asmawati Asmawati Bambang Satrio Gandhi Cholid Fadilah Hasri Danuarta, Ervin Dedi Darwis Dina Amelia Dinda Titian Lestari Dyah Aminatun Dyah Ayu Megawaty Dyah Ayu Megawaty Emi Suwarni Erliyan Redy Susanto Fadila Shely Amalia Feby Amanda Fenty ariany Handika, H Hasrina, Cut Delsi Hayatunnufus Hayatunnufus Heni Sulistiani Ika Septiana Imam Ahmad Imam Ahmad Indah Sari Irwan Tubagus Isnain, Auliya Rahman Jepi Supriyanto Khoirin Nisa Krisna, Anan Lathifah L Lathifah Lathifah Lisa Ariyanti M Febrian As Shidiq Mandasari, Berlinda Marga, Nurman Satya Miswanto Miswanto Muhammad Najib Dwi Satria Muhammad Pajar Kharisma Putra Muhammad Saefudin Muhtad Fadly Munti Sarida Nur Rokhman Nurhasan Nugroho Nurman Satya Marga Nuroji Nuroji Permata Permata Prita Dellia Puspaningtyas, Nicky Dwi Pustika, Reza Putra, Ade Dwi Putri Sukma Dewi Putri, Vanisa Adellia Rahmadila, Selvi Rahmawati, Ira Tri Rani, Ni Made Suka RB Ali Shodiqin Rillya Arundaa Riska Amalia Praptiwi S. Samsugi Sakti, Adam Indra Salsabilla, Tasya SANTIKA, YULI Saputri, Gita Aprinda Sari, Raras Kartika Sena, Fikih Yuhada Setiawansyah Setiawansyah Sigit Priyanta Styawati Styawati Styawati Styawati, Styawati suaidah suaidah Suhadi, Miki Surahman, Ade Suwarni, Emi Syahwaluddin, Risal Taufiq Martiwansyah Tenda, Edwin Wang, Junhai YOHANA TRI UTAMI, YOHANA TRI Yuri Rahmanto Yusra Fernando