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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknik Komputer AMIK BSI Jurnal Khatulistiwa Informatika Bina Insani ICT Journal Jurnal Pilar Nusa Mandiri Faktor Exacta Jurnal Teknik Informatika STMIK Antar Bangsa Jurnal Mantik Penusa JISICOM (Journal of Information System, Infomatics and Computing) Journal of Information System, Applied, Management, Accounting and Research EVOLUSI : Jurnal Sains dan Manajemen INTI Nusa Mandiri JATI (Jurnal Mahasiswa Teknik Informatika) SPEED - Sentra Penelitian Engineering dan Edukasi J-CEKI
Elly Indrayuni
Universitas Bina Sarana Informatika
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Social Sciences

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Penerapan Metode Waterfall Dalam Cetak Desain Produk Pada CV. Thomi Putra Sejahtera Jakarta Achmad Nurhadi; Elly Indrayuni - UBSI
SPEED - Sentra Penelitian Engineering dan Edukasi Vol 11, No 4 (2019): Speed 2019
Publisher : APMMI - Asosiasi Profesi Multimedia Indonwsia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1046.174 KB) | DOI: 10.55181/speed.v11i4.622

Abstract

Abstrak— CV.Thomi Putra Sejahtera yang saat ini begerak pada bidang percetakan dan desain produk, masih menggunakan cara yang konvensional yaitu pembeli harus datang secara langsung kedalam toko untuk melakukan pemesanan sehingga pembeli yang jauh dari toko kesulitan untuk melakukan pemesanan, begitu juga untuk melakukan pemasaran pihak toko masih menggunakan brosur yang membutuhkan biaya yang cukup besar, lalu penulisan laporan transaksi yang masih menggunakan buku kas yang memungkinkan buku itu menjadi rusak maupun hilang, dibutuhkan suatu perubahan sistem agar sistem menjadi lebih efesien yaitu menggunakan website dimana memudahkan pihak toko maupun pembeli untuk melakukan pemesanan, pemasaran maupun pembuatan laporan yang nantinya akan tersimpan kedalam database. Penelitan ini menggunakan beberapa tahapan metode waterfall yaitu: analisis kebutuhan perangkat lunak, desain, dan pembuatan kode program. Kata kunci— Desain produk, website, waterfall  Abstract—CV. Thomi Putra Sejahtera, which is currently working in the field of printing and product design, still uses a conventional method, namely the buyer must come directly into the store to place an order so that buyers far from the store have difficulty making orders, as well as shop marketing still using brochures that require considerable costs, then writing transaction reports that still use cash books that allow the book to be damaged or lost, a system change is needed so that the system becomes more efficient that is using a website that makes it easier for the store and the buyer to place an order. Marketing and making reports that will later be stored in the database. This research uses several stages of the waterfall method, namely: software requirements analysis, design, and programming code. Keywords— Product design, website, waterfall
Work schedule system application at PT. Asima Jaya Teknik Bekasi Acmad Nurhadi; Elly Indrayuni
Journal of Information System, Informatics and Computing Vol 6 No 2 (2022): JISICOM: December 2022
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v6i2.969

Abstract

With the Covid-19 virus still making us have to innovate in the field of technology to support a job. and to find out the problems that exist in PT. Asima Jaya Teknik, the method used is the waterfall method, where the Waterfall Model is divided into four stages, requirements analysis, design, coding, and testing. PT. Asima Jaya Teknik, in the process of recap work it is still not computerized so that in carrying out the process, errors are still encountered starting from when inputting data to getting work to billing data. The process is still manual using paper, so there is often loss and damage, sometimes between one employee and another having data that is not the same, so they have to work twice to correct data from other employees. The results of this study create an application that is used to recap the work of employees that can help and alleviate and speed up the work process at PT. Asima Jaya Engineering. In addition, it can also save time and energy from the employees of PT. Asima Jaya Engineering.
OPTIMASI NAIVE BAYES BERBASIS PSO UNTUK ANALISA SENTIMEN PERKEMBANGAN ARTIFICIAL INTELLIGENCE DI TWITTER Elly Indrayuni; Acmad Nurhadi
INTI Nusa Mandiri Vol 18 No 1 (2023): INTI Periode Agustus 2023
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v18i1.4282

Abstract

At present the development of Artificial Intelligence technology is progressing rapidly. There are many new artificial intelligence technologies available in various fields. Artificial Intelligence is an artificial intelligence program that can study data, perform processes of thinking and acting like humans. The presence of Artificial Intelligence technology has many positive impacts, especially in increasing work effectiveness and efficiency. However, AI is also a threat to human resources because slowly human work is being replaced by Artificial Intelligence. Various opinions about the development of Artificial Intelligence are widely discussed on social media such as Twitter. Sentiment analysis is a computational study to automatically categorize opinions into positive or negative categories. In this study, the Naive Bayes algorithm was used to analyze sentiment or public opinion regarding the development of Artificial Intelligence for Twitter users. The data collection method used is crawling data on Twitter. The results of the sentiment classification test for the development of Artificial Intelligence using Naive Bayes yield an accuracy value of 86.42%. Meanwhile, the results of the sentiment classification test using Naive Bayes based on Particle Swarm Optimization (PSO) increased with an accuracy value of 87.55%. Based on the results of this study, the use of PSO as an optimization technique for the Naive Bayes algorithm is proven to be the best algorithm model in sentiment analysis for the development of Artificial Intelligence for English text.
Aplikasi E-Bootcamp Sebagai Pengembangan Media Pelatihan Berbasis Mobile dan Website Achmad Nurhadi; Elly Indrayuni
JURNAL TEKNIK KOMPUTER AMIK BSI Vol 10, No 1 (2024): Periode Januari 2024
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jtk.v10i1.19377

Abstract

Menurut PP No. 31 Tahun 2006 tentang Sistem Pelatihan Kerja Nasional, pelatihan kerja adalah keseluruhan kegiatan untuk memberi, memperoleh, meningkatkan, serta mengembangkan kompetensi kerja, produktivitas, disiplin, sikap, dan etos kerja pada tingkat keterampilan dan keahlian tertentu sesuai dengan jenjang dan kualifikasi jabatan atau pekerjaan. Seiring berjalannya waktu, banyak sekali pelatihan-pelatihan pemrograman atau yang biasa disebut Programming Bootcamp terbentuk. Dalam hal tersebut, Bootcamp sendiri biasanya memiliki konsentrasi khusus dalam materi pembelajarannya, contohnya seperti Bootcamp Juaracoding yang memiliki konsentrasi dalam pembelajaran Fullstack Developer Java Android Programming. Pada awal mula ide berdirinya Bootcamp itu sendiri, sistem manajemennya masih menggunakan penginputan manual melalui software yang biasa disebut Microsoft Excel, sehingga tidak efektif untuk digunakan dalam mengelola data Trainee yang ada. Penggunaan metode Waterfall dianggap mampu untuk menjadi salah satu pilihan dari sekian banyak metode pengembangan perangkat lunak yang terdapat pada SDLC (Software development Life Cycle) dalam menterjemahkan kebutuhan user dan memberikan solusi yang baik saat penerapannya. Dan pengembangan media pelatihan berbasis mobile apps adalah produk yang berhasil dikembankan untuk mendukung pelatihan-pelatihan yang saat ini sudah berbasis teknologi dengan memanfaatkan smartphone berbasis android.
Sentiment Analysis About COVID-19 Booster Vaccine on Twitter Using Deep Learning Elly Indrayuni; Achmad Nurhadi
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11485

Abstract

The rapid spread of COVID-19 cases to various countries has made the COVID-19 outbreak a global pandemic by the World Health Organization (WHO). The effect of the designation of COVID-19 as a pandemic has prompted the government to take preventive action against vaccination, as well as the WHO which has asked the public to immediately get a third or booster dose of vaccine. Various responses regarding the COVID-19 booster vaccine continue to emerge on social media such as Twitter. Twitter is often used by its users to express emotions about something either positive or negative. People tend to believe what they find on social networks, which makes them vulnerable to rumors and fake news. Sentiment analysis or opinion mining is one solution to overcome the problem of automatically classifying opinions or reviews into positive or negative opinions. In this study, the Deep Learning algorithm was used to analyze public opinion sentiment regarding the COVID-19 booster vaccine on Twitter. The data collection method used is crawling data using an access token obtained from the Twitter API. Meanwhile, to evaluate the model, the K-fold Cross-Validation method is used. The results of testing the model obtained the highest accuracy value at iterations = 10, which is 82.78% with AUC value = 0.836, precision = 83.33% and recall = 95.89%.
KOMPARASI ALGORITMA NAIVE BAYES DAN SUPPORT VECTOR MACHINE UNTUK ANALISA SENTIMEN REVIEW FILM Indrayuni, Elly
Jurnal Pilar Nusa Mandiri Vol 14 No 2 (2018): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Septemb
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1073.511 KB) | DOI: 10.33480/pilar.v14i2.36

Abstract

Film is a subject of interest by a large number of people among the social networking community who have significant differences in their opinions or sentiments. Sentiment analysis or opinion mining is one solution to overcome the problem to classify opinions or reviews into positive or negative opinions automatically. The technique used in this study is Naive Bayes and Support Vector Machines (SVM). Naive Bayes has advantages that are simple, fast and have high accuracy. Whereas SVM is able to identify a separate hyperplane that maximizes the margin between two different classes. The results of the sentiment classification in this study consisted of two class labels, namely positive and negative. The value of accuracy produced will be a benchmark for finding the best testing model for sentiment classification cases. Evaluation is done using 10 fold cross validation. Accuracy measurements were measured by confusion matrix and ROC curve. The results showed that the accuracy value for the Naive Bayes algorithm was 84.50%. While the accuracy value of the Support Vector Machine (SVM) algorithm is greater than Naive Bayes which is equal to 90.00%.
ANALISIS KEPUASAN PELAYANAN MUTASI PEGAWAI MENGGUNAKAN METODE FUZZY SERVICE QUALITY (SERVQUAL) Indrayuni, Elly
Jurnal Pilar Nusa Mandiri Vol 13 No 2 (2017): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Septemb
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (819.733 KB)

Abstract

Mutation is a change of position the post, a place or employment inside an organization or company that can occur over my own request or request of the company. But there were several problems in the service of mutation employees placement of them is the position of civil servants is in accordance with capacity so that may affect employee motivation work and also the effectiveness of our performance on the company. The purpose of this research is to find satisfaction service employees whether a mutation is according to competence of employees. Methods used in this research is fuzzy service quality (servqual). The results of the analysis method servqual consisting of 52 variables with 5 dimensions that the variable that need undergoing repair namely empathy (emphaty), physical evidence (tangible) and dependability (reliability). The highest the gap shown in dimension sensibility (responsivess) namely 0,034 there is a positive influence and the lowest gap shown in dimension dependability (reliability) namely -0,054 is the negative to the quality of services and satisfaction employees to mutation service employees.
IMPLEMENTASI TEKNIK SMOTE UNTUK MENGATASI IMBALANCE CLASS DALAM KLASIFIKASI SENTIMEN MENGENAI CHATGPT Indrayuni, Elly; Nurhadi, Acmad
INTI Nusa Mandiri Vol. 19 No. 1 (2024): INTI Periode Agustus 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i1.5595

Abstract

ChatGPT is a chatbot or computer program in the form of a virtual robot that can simulate human-like conversations. ChatGPT is widely used in various fields in academia. The impact of the use of ChatGPT on academia and public perception of this technology is significant. Sentiment analysis can be used to determine the polarity of a text or opinion that is positive or negative. In this research, social media is used as a data source to collect public opinion regarding ChatGPT instantly. The methods used in this reserach are the KNN algorithm and Naive Bayes algorithm. The aim of this research is to find the best algorithm model for sentiment classification in terms of public opinion for ChatGPT which contains English text. Before testing the algorithm model, a text processing stage was carried out which included the processes of case folding, tokenizing, stopword removal, and stemming. Word weighting using TF-IDF was carried out before the data was ready to be processed. Splitting data used in this research includes 80% of the dataset as training data and 20% of the dataset as testing data. The application of the SMOTE technique to the KNN and Naive Bayes algorithms to overcome the imbalance class of the public opinion dataset regarding ChatGPT. The research results show that combining SMOTE and Naive Bayes algorithm gives the best results with an accuracy value of 85.00%, a precision value of 87.64%, a recall value of 84.78% and an f1-score of 86.18%.
PENERAPAN USER EXPERINCE QUESTIONAIRE (UEQ) PADA PENGUKURAN EFEKTIFITAS APLIKASI SATUSEHAT Nurhadi, Acmad; Indrayuni, Elly
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 6 (2024): JATI Vol. 8 No. 6
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i6.11638

Abstract

SATUSEHAT adalah sebuah platform aplikasi yang dahulu Bernama Peduli Lindungi saat wabah Covid-19 melanda, namun sekarang berubah menjadi SATUSEHAT yang bertujuan untuk mempermudah akses masyarakat dalam mencari informasi seputar kesehatan, seperti artikel, jadwal praktik dokter, dan pemesanan obat. Sepanjang penggunaan aplikasi tersebut, belum ada pembahasan mengenai efektifitas aplikasi tersebut sehingga Penggunaan metode User Experience Questionnaire (UEQ) dilakukan untuk mengevaluasi pengalaman pengguna dalam menggunakan SATUSEHAT. Hasil dari penelitian didapat Pengujian validitas sudah valid dengan nilai r hitung lebih besar dari r tabel yaitu rata-rata diatas 0.195, sedangkan hasil pengujian reliability didapatkan nilai Cronbach Alpha antara 0 dan 1 yaitu rata-rata 0.750 dan untuk perhitungan skala UEQ atribut tertinggi yaitu Daya Tarik (Attractiveness) memiliki skor mean 0,833 dan varians 1,34, sedangkan atribut terendah Kebaruan (Novelty) memiliki mean 0,520 dan varians 1,26. Sehingga pada hasil menggunakan metode User Experience Questionare (UEQ) dari beberapa aspek ada tergolong nilainya paling rendah diantara nilai aspek lainnya yaitu aspek Kebaruan (Novelty), maka perlu adanya evaluasi pada aspek kebaruan demi meningkatkan dan kenyamanan kepuasan pengguna. Selain itu, metode UEQ juga dapat digunakan untuk mengevaluasi pengalaman pengguna dalam aplikasi Kesehatan lainnya, sehingga penggunaan metode ini memiliki potensi yang luas dalam bidang pengembangan aplikasi berbasis Kesehatan.
ANALISIS SENTIMEN APLIKASI TIKTOK SHOP SELLER CENTER MENGGUNAKAN NAIVE BAYES, SVM DAN LOGISTIC REGRESSION Indrayuni, Elly; Acmad Nurhadi
INTI Nusa Mandiri Vol. 20 No. 1 (2025): INTI Periode Agustus 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v20i1.6851

Abstract

The rapid growth of e-commerce has driven the emergence of new platforms such as TikTok Shop Seller Center, which is now integrated with Tokopedia. Increasing competition among digital platforms has made service quality and user experience key success factors. In this context, user reviews and feedback serve as crucial data sources that reflect satisfaction, complaints, and expectations toward the application. However, the large and diverse volume of reviews renders manual analysis inefficient. Therefore, an automated approach such as sentiment analysis is required to classify user opinions quickly and accurately. This study aims to perform sentiment analysis on TikTok Shop Seller Center user reviews using Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression algorithms to determine the best-performing model. The dataset was obtained from the Kaggle platform and underwent preprocessing, including case folding, tokenization, stemming, and TF-IDF weighting. Model evaluation was conducted using confusion matrix and ROC curve, along with performance metrics such as accuracy, precision, recall, and F1-score. The results show that the SVM algorithm outperformed Naïve Bayes and Logistic Regression, achieving 93.75% accuracy, 93.78% precision, 95.65% recall, 94.70% F1-score, and an AUC of 0.98, categorized as Excellent Classification. Thus, SVM proved to be the most effective algorithm for classifying user review sentiments on TikTok Shop Seller Center.
Co-Authors Achmad Nurhadi Achmad Nurhadi Acmad Nurhadi Belsana Butar Butar Deigo Anugrah Pratama Dinar Ajeng Kristiyanti Ibnu Rizal Mutaqin Muyasar Kevin Rafael Manuela Feltya