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All Journal Jurnal Masyarakat Informatika PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Algoritma Jurnal Teknologi Informasi dan Ilmu Komputer REKAYASA Jurnal Ecodemica : Jurnal Ekonomi Manajemen dan Bisnis Jurnal Informatika Jurnal Pilar Nusa Mandiri Forum Ekonomi : Jurnal Ekonomi, Manajemen dan Akuntansi JURNAL MANAJEMEN Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) INTI Nusa Mandiri Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences IAIC Transactions on Sustainable Digital Innovation (ITSDI) Tridharmadimas: Jurnal Pengabdian Kepada Masyarakat Jayakarta International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Algoritma International Journal of Advanced Science Computing and Engineering Jurnal Pengabdian Ibnu Sina Journal Medical Informatics Technology SmartComp
Dwiza Riana
Institut Teknologi Bandung
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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PELATIHAN PEMANFAATAN AI UNTUK MENUNJANG PENINGKATAN LITERASI DIGITAL Pardede, Hilman F; Riana, Dwiza; Kurniawati, Laela; Ernawan, Ferda; Na'am, Jufriadif
TRIDHARMADIMAS: Jurnal Pengabdian Kepada Masyarakat Jayakarta Vol 4 No 2 (2024): PKM-TRIDHARMADIMAS (Desember 2024)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/tridharmadimas.v4i2.1719

Abstract

. Dengan adanya perkembangan teknologi informasi dan komunikasi yang semakin meningkat saat ini, memiliki keterampilan literasi digital menjadi suatu kebutuhan mendesak untuk mempersiapkan generasi muda menghadapi tantangan teknologi. Artificial intelligent hadir untuk membantu pekerjaan manusia hingga dapat menyelesaikan tugasnya dengan cepat, tepat, efektif dan efisien, bukan untuk menggantikan pekerjaan manusia. Mitra dalam program ini adalah Jaringan Pemuda dan Remaja Indonesia (JPRMI) DKI Jakarta. JPRMI DKI Jakarta bertempat di Jl. Jend. Basuki Rachmat No.1A, RT.1/RW.9, Bidara Cina, Kecamatan Jatinegara, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta 13410. JPRMI merupakan organisasi sosial dengan keanggotaan pemuda masjid di wilayah DKI Jakarta. Permasalahan yang selama ini dihadapi oleh mitra adalah kesulitan dalam memberikan pengarahan kepada peserta terkait bagaimana pemanfaatan AI untuk menunjang peningkatan literasi digital. Solusi untuk mitra dalam mengatasi permasalahan tersebut adalah memberikan pelatihan pemanfaatan AI untuk menunjang peningkatan literasi digital. Berdasarkan permasalahan tersebut Fakultas Teknologi Informasi Universitas Nusa Mandiri akan menyelenggarakan Tri Dharma Perguruan Tinggi yaitu kegiatan pengabdian masyarakat dengan tema Pelatihan Pemanfaatan AI untuk Menunjang Peningkatan Literasi Digital. Kegiatan PKM ini bertujuan untuk mengembangkan pengetahuan dan wawasan peserta terkait pemanfaatan AI untuk menunjang peningkatan literasi digital, dengan luaran yang ditargetkan dari kegiatan ini adalah publikasi release dan submit ke jurnal nasional
Evaluating Deep Learning Architectures for Potato Pest Identification: A Comparative Study of NasNetMobile, DenseNet, and Inception Models Hadianti, Sri; Riana, Dwiza; Sulistyowati, Daning Nur
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.545

Abstract

Manual potato pest identification that is still applied today is often time-consuming and highly dependent on farmer skills in the field. This causes delays in taking action and inaccurate reporting, especially in pest emergencies. In addition, these limitations slow down the response to pest control which ultimately risks reducing crop yields and farmer income. This study aims to develop a more accurate, fast, and consistent deep learning-based approach to identify potato pests, in order to support practical solutions that farmers can implement independently. This study contributes by comparing three deep learning architecture models, namely NasNetMobile, DenseNet, and Inception which are designed to identify pest images. The potato pest image dataset used was collected from various sources equipped with an augmentation process to increase data diversity. The model was drilled using transfer learning techniques to utilize previously learned features on a large dataset. The evaluation model was carried out comprehensively based on accuracy, precision, and inference time efficiency. The results showed that the DenseNet model achieved the highest accuracy of 97% with an inference time of 11 seconds, and this model maintained a relatively stable performance and was superior several times compared to other models. Based on these results, DenseNet was chosen as the most effective and reliable model to be developed for practical applications in the field. This study provides practical implications in the form of providing a model that can be integrated into a mobile-based application that is easy to use by farmers, including in remote areas. This allows farmers to identify pests independently without requiring in-depth technical expertise. In addition, this study is a new benchmark for the development of artificial intelligence-based pest identification systems in other crops and opens up opportunities for integration with IoT-based technologies to support sustainable agricultural practices.
Analysis of User Complaints for Telecommunication Brands on X (Twitter) using IndoBERT and Deep Learning Hakim, Valianda Farradillah; Riana, Dwiza
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 2 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i2.76497

Abstract

Tweeting on different official accounts is what users of Twitter (X) do most frequently. These tweets ranging from compliments to critiques. One of the official accounts that gets a lot of tweets from its customers is Telkomsel, an Indonesian telecom company. This study aims to find the maximum accuracy that can be obtained by combining CNN and Bi-LSTM algorithms with IndoBERT embeddings. A considerable accuracy level above 90% is demonstrated by the study, with CNN obtaining the greatest accuracy of 99% at a learning rate of 6*10^-5, along with scores of 98%, 97%, and 97% for precision, recall, and F1 correspondingly.
Disease Identification on Fig Leaf Images Using Deep Learning Method Bismi, Waeisul; Riana, Dwiza; Hewiz , Alya Shafira
International Journal of Advanced Science Computing and Engineering Vol. 6 No. 2 (2024)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.6.2.203

Abstract

The fig plant, known as Ficus carica, has been cultivated worldwide, including in Indonesia. It has nutritional benefits and medicinal properties. However, there are still difficulties in growing it, making the plant scarce. The scarcity of fig plants in Indonesia is mainly due to the threat of diseases and viruses that affect them. Various diseases affect fig plants, including leaf rust (Cerotelium fici), mosaic disease, and Bemisia tabaci (whitefly) disease. Infected fig plants become unhealthy, experiencing stunted growth and deformed fruits; thus, it is necessary to identify the diseases accurately using technological assistance. This research aimed to identify diseases in fig leaves automatically. The method began by digitizing fig leaf images and consulting botanical experts specializing in fig plants to determine the types of diseases present. The research produced a dataset of fig leaf images consisting of four classes of fig leaves: Cerotelium fici, mosaic disease, whitefly, and healthy fig leaves. The dataset resulted in the confirmation of 300 fig leaf images. The augmentation techniques were applied to increase the number of images to 3,300 fig leaf images. This dataset was then divided into subsets for training, validation, and testing. For the classification and identification, a Deep Learning approach was used with three models: VGG16, VGG19, and MobileNet. Among these models, MobileNet achieved the highest accuracy of 98.79%. Subsequently, the identification system was implemented by converting the generated model into TensorFlow Lite and integrating it into the Android Studio software, enabling it to function as a mobile application on Android devices.
SOSIALISASI KESADARAN KEAMANAN SIBER PADA BADAN SANTUNAN YATIM KELURAHAN PONDOK CINA DEPOK Riana, Dwiza; Ernawan, Ferda; Na'am, Jufriadif; Pardede, Hilman Ferdinandus; Hasanah, Riyan Latifahul
Jurnal Pengabdian Ibnu Sina Vol. 4 No. 1 (2025): Januari 2025
Publisher : LPPM Universitas Ibnu Sina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/j-pis.v4i1.848

Abstract

ABSTRAK Perkembangan teknologi informasi dan komunikasi selain membawa manfaat tapi juga berdampak dengan munculnya tindak pidana baru dalam bidang teknologi. Modus kejahatan dalam bidang teknologi (cyber chrime) mengikuti alur yang terjadi dalam sistem digital. Lima jenis penipuan yang sering terjadi yaitu, penipuan berkedok hadiah, pinjaman digital ilegal, pengiriman tautan yang berisi malware atau virus, penipuan berkedok krisis keluarga, dan investasi ilegal. Dalam rangka melaksanakan kegiatan Tri Dharma Perguruan Tinggi yaitu Pengabdian kepada Masyarakat, Fakultas Teknologi Informasi Universitas Nusa Mandiri menyelenggarakan pelatihan dengan tema “Sosialisasi Kesadaran Keamanan Siber pada Badan Santunan Yatim Kelurahan Pondok Cina Depok”. Kegiatan ini bertujuan untuk meningkatkan kesadaran peserta akan pentingnya keamanan siber serta menambah wawasan mengenai keamanan siber. Luaran pengabdian kepada masyarakat berupa press release yang ditayangkan di Nusa Mandiri News serta artikel jurnal. Kata Kunci: keamanan siber, pengabdian kepada masyarakat, sosialisasi ABSTRACT The development of information and communication technology not only brings benefits but also has an impact on the emergence of new criminal acts in the field of technology. The mode of crime in the field of technology (cyber crime) follows the flow that occurs in digital systems. The five types of fraud that often occur are, fraud under the guise of gifts, illegal digital loans, sending links containing malware or viruses, fraud under the guise of family crises, and illegal investments. In order to carry out the Tri Dharma of Higher Education activities, namely Community Service, the Faculty of Information Technology, Nusa Mandiri University held training with the theme "Socialization of Cyber ​​Security Awareness in the Orphan Compensation Agency, Pondok Cina Village, Depok". This activity aims to increase participants' awareness of the importance of cyber security and increase their insight into cyber security. The output of community service is in the form of press releases published in Nusa Mandiri News and journal articles. Keywords: cyber security, community service, socialization
Market Basket Analysis untuk Penjualan Retail: Perbandingan Akurasi Algoritma Apriori dan FP-Growth Berbasis CRISP-DM Rahman, Irfan Fadholur; Riana, Dwiza
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2303

Abstract

Increasing the efficiency of sales strategies and product stock management is a major requirement in the retail business, including in the sale of school uniforms. This research aims to identify consumer purchasing patterns through the application of the Market Basket Analysis method using two data mining algorithms, namely Apriori and Frequent Pattern Growth (FP-Growth). The approach used is CRISP-DM, consisting of six main stages, with a dataset of 365 sales transactions and minimum support parameters of 2% and confidence of 60%. The results showed that the Apriori algorithm generated association rules with an accuracy rate of 63.19%, average confidence of 75%, and support of 4.5%, while FP-Growth only achieved an accuracy of 2.92%. This finding shows that in the context of school uniform sales transaction data, Apriori is superior in exploring consumer purchasing patterns. The practical contribution of this research is the recommendation of product bundling and stock optimization strategies based on actual association patterns, which can be applied by educational retail businesses to improve business efficiency and effectiveness.
PENERAPAN K-MEANS DAN K-MEDOIDS BERBASIS RFM PADA SEGMENTASI PELANGGAN DI MASA PANDEMI COVID-19 Watmah, Sri; Riana, Dwiza; Astuti, Rachmawati Darma
INTI Nusa Mandiri Vol. 18 No. 2 (2024): INTI Periode Februari 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v18i2.4963

Abstract

The outbreak of the CORONA virus in Indonesia in early March 2020 has created unrest, especially in the business world. The impact caused some small and medium-sized businesses to go out of business, so the right marketing strategy is needed to maintain and increase customer loyalty. The purpose of this research is to segment PT Megadaya Maju Selaras' customers based on their characteristics by comparing the RFM-based K-Means and K-Medoids algorithms as attributes in the research. The dataset used comes from the purchase transaction data of PT Megadaya Maju Selaras customers. Experiments in this study used the CRISP-DM model. The results showed that the K-Means algorithm has a smaller Davies Bouldin Index (DBI) value than K-Medoids, meaning that the K-Means method is the right method for this research. With the K-Means method, the overall data shows the optimal k in cluster 4 with a DBI value of 0.286, the data before the pandemic shows the optimal k value in cluster 2 with a DBI value of 0.299, after the pandemic shows the optimal k in cluster 5 with a DBI value of 0.278. The overall data is divided into 4 segments, namely superstar, typical customer, occational customer and dormant customer. Data before the pandemic is divided into 2 segments, namely typical customers and superstars. Meanwhile, after the pandemic is divided into 5 segments, namely typical customer, occational customer, golden customer, dormant customer and superstar. With this research, PT Megadaya Maju Selaras can provide the right service for each customer group.
Pemanfaatan Aplikasi Pengiriman Makanan Pasca Penurunan Level Pembatasan Kegiatan Masyarakat Akibat Covid-19 Di Indonesia Kodri, Wan Ahmad Gazali; Riana, Dwiza; Hadianti, Sri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 3: Juni 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023106859

Abstract

Penggunaan aplikasi pengiriman makanan meningkat sangat cepat, terlebih saat terjadinya Pandemi COVID-19, dimana pergerakan orang dibatasi, membuat setiap orang berupaya menggunakan aplikasi pengiriman makanan atau Food Delivery Application (FDA) dalam memenuhi kebutuhan pangan. Penurunan jumlah kasus COVID-19 menyebabkan pemerintah Indonesia menurunkan level Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM) sehingga masyarakat dapat beraktivitas sosial kembali. Tujuan penelitian ini adalah untuk menilai instrumen yang memengaruhi Continuance Intention FDA pasca penurunan level PPKM COVID-19 menjadi level 1 di Indonesia. Sebanyak 166 responden telah dikumpulkan. Kuesioner terdari dari 17 pertanyaan demografi dan 38 pertanyaan indikator. Skala Likert dengan lima tingkat penilaian digunakan untuk mengevaluasi pertanyaan indikator. Model yang digunakan adalah Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Data dianalisis dengan menggunakan Structural Equation Modeling (SEM) berbasis Partial Least Square (PLS), meliputi analisis faktor, analisis jalur, dan regresi. Penelitian menunjukkan Performance Expectancy, Social Influence, Habit, dan Rasa Solidaritas berdampak signifikan pada Continuance Intention FDA. Effort Expectancy, Facilitating Condition, Hedonic Motivation, Price Value, dan Risk Perception menunjukkan pengaruh yang tidak signifikan terhadap Continuance Intention. Pengembang FDA dapat menggunakan data ini untuk meningkatkan layanan mereka dan menambah pemahaman tentang FDA, loyalitas pengguna, peluang bisnis dan strategi pemasaran. Restoran dapat menggunakan kajian ini untuk melihat pergeseran pola pembelian makanan. AbstractThe use of food delivery applications is increasing very quickly, especially during the COVID-19 Pandemic, when people's movements were restricted, making everyone try to use Food Delivery Applications (FDA) to meet their meal needs. The decrease in the number of COVID-19 cases has caused the Indonesian government to lower the level of Enforcement of Restrictions on Community Activities (PPKM) so that people can return to common social activities. The purpose of this study was to assess the instruments that influence the FDA's Continuance Intention after the reduction in the level of PPKM COVID-19 to level 1 in Indonesia. A total of 166 respondents have been collected. The questionnaire consists of 17 demographic questions and 38 indicator questions. A Likert scale with five rating levels was used to evaluate the indicator questions. The model used is the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Data were analyzed using Partial Least Square (PLS) based Structural Equation Modeling (SEM), including factor analysis, path analysis, and regression. Research shows Performance Expectancy, Social Influence, Habit, and Sense of Solidarity have a significant impact on FDA Continuance Intention. Effort Expectancy, Facilitating Condition, Hedonic Motivation, Price Value, and Risk Perception show no significant effect on Continuance Intention. FDA developers can use this data to improve their services and increase their understanding of the FDA, user loyalty, and identify marketing opportunities and strategies. Restaurants can use this assessment to see shifts in food purchasing patterns. 
TEXT CLASSIFICATION USING INDOBERT FINE-TUNING MODELING WITH CONVOLUTIONAL NEURAL NETWORK AND BI-LSTM Zevana, Alda; Riana, Dwiza
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.1650

Abstract

The technological advancements in goods delivery facilities have been increasing year by year in tandem with the growing online trade, which necessitates delivery services to fulfill the transactional process between sellers and buyers. Since 2000, top brand awards have often conducted official survey analyses to provide comparisons of goods or services, one of which includes delivery services. However, the survey rankings based on public opinion are less accurate due to users of delivery services and service companies being unaware of the specific success factors and weaknesses in their services. The aim of this research is to analyze the comparison of text mining using the Indonesian language transformation method, IndoBert. The algorithm utilized to demonstrate analysis performance employs Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). This method is utilized to determine the impact of opinion data from Twitter on the J&T Express expedition delivery service, incorporating both text preprocessing and data without text preprocessing. The IndoBert parameters vary in the learning rate section based on four factors: price, time, returns, and others. The research data consisted of 2525 comments from Twitter users regarding the delivery service spanning from January 1, 2021, to March 31, 2023. The testing showed that Bi-LSTM with text preprocessing performed 2% higher, achieving 79% at a learning rate of 1x10-6, compared to without text preprocessing at the same learning rate, which reached 77%. Additionally, CNN outperformed by 3% with a rate of 83%, compared to 80% without text preprocessing at a learning rate of 1x10-5. The highest accuracy, reaching 83%, was obtained by CNN with parameters set at 1x10-5, and the preprocessing technique was considered superior to Bi-LSTM.
Mengukur Niat Berkelanjutan Penggunaan Mobile Banking Pasca Pandemi Berdasarkan Analisa dan Survey Pengguna Rumintarsih, Ade; Riana, Dwiza; Hardianti, Sri
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 13, No 1 (2024): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v13i1.5375

Abstract

Kebijakan pembatasan sosial dan jarak selama pandemi membuat semakin banyak orang mulai menggunakan layanan pembayaran seluler. Ini karena fitur pembayaran seluler (Mobile Banking) yang mudah dan bebas kontak.  Mudah digunakan dan dapat dilakukan dari mana saja tanpa ada kontak fisik sesama pengguna. Hal ini menyebabkan peningkatan jumlah masyarakat Indonesia yang menggunakan layanan ini, Bahkan Pasca pandemi, tren ini tidak berkurang, nyatanya pengguna aplikasi ini terus berkembang. Studi ini bertujuan untuk menyelidiki secara komprehensif faktor niat pengguna Mobile Banking pasca pandemi untuk memperluas domain adopsi teknologi pasca-pandemi. Dengan mengintegrasikan model teoritis Delone dan McLean bersama dengan dua variabel tambahan dari ECM (Confirmed and Perceived Usefulness). Data yang dikumpulkan dari responden  mengungkapkan bahwa persepsi pengguna tentang manfaat aplikasi mempengaruhi penerimaan mereka terhadap layanan Mobile Banking. Orang lebih cenderung untuk terus menggunakan layanan jika mereka mempercayai pengguna dan merasakan manfaatnya. Selain itu, pengguna yang mengadopsi layanan dapat dipengaruhi oleh kepuasan dan manfaat yang dirasakan.
Co-Authors Abbas, Moch Anwar Ahmad Azhari Anton Anton Arif Budiarto Astuti, Rachmawati Darma Aziz, Faruq Choerunisya, Hana Dwi Saputra, Heru Dwinanda, Brama Rizky Setia Edi Sabara Efendi, Ade Irfan Efendi eka rahmawati Ferda Ernawan Gata, Windu Hakim, Valianda Farradillah Hasanah, Riyan Latifahul Herwanto, Teguh Hewiz , Alya Shafira Hewiz, Alya Shafira Ilhami, Azriel Noer Jamal Maulana Hudin Jamil, Muh. Jumadi, J Kahfi, Ahmad Hafidzul Kodri, Wan Ahmad Gazali Kurniawantoro Laela Kurniawati, Laela Luh Putu Ratna Sundari Mahendra Mahendra, M Muhamad Hasan Muhammad Qomaruddin Na'am, Jufriadif Nur Sulistyowati, Daning Nurhasanah, Fitri Yani Octaviantara, Adi Pardede, Hilman F Pardede, Hilman Ferdinandus Rahman, Irfan Fadholur Ramanudin, Eko Rudini, Edwin Rumintarsih, Ade Sahidillah Nurdin Saputra, Ridwan Saputra, Surya Fajar Sri Hadianti Sri Hardianti Sulistyowati, Daning Nur Susilawati, Desi Susilawati Thira, Indra Jiwana Waeisul Bismi Wati, Elga Watmah, Sri Widagdo, Galih Widanto, Frantina Andri Widianto, Tetra Wildan Wiguna WITRIANA ENDAH PANGESTI Zevana, Alda