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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JOIV : International Journal on Informatics Visualization Jurnal Pilar Nusa Mandiri Jurnal Teknik Informatika STMIK Antar Bangsa JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Jurnal Informasi dan Teknologi Community Development Journal: Jurnal Pengabdian Masyarakat JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Dinar Ajeng Kristiyanti, Dinar Ajeng
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Health Professions Public Health Other

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E-COMMERCE MERCHANDISE KAMPUS PADA PT. COME INDONUSA JAKARTA MENGGUNAKAN UNIFIED MODELING LANGUAGE (UML) Kristiyanti, Dinar Ajeng
Jurnal Teknik Informatika Vol 1 No 1 (2015): JTI Periode Februari 2015
Publisher : LPPM STMIK Antar Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51998/jti.v1i1.27

Abstract

Abstract— E-Commerce (Electronic Commerce) has become the trend since the emerge of thousand companies who sell their wares on the website. The increasing of fierce competition makes both companies and individuals endeavors to provide convenience facilities for the consumer or user in finding out the data item as long as the terms order on the website, so that the customer of the user is interested to buy the product. PT. COME Indonusa Jakarta is a company focus in selling goods. Both selling service and business as well as the sale of particular goods, campus merchandise. As for the goods produced to date is the merchandise of the Academy of Bina Sarana Informatika. In the long term of business plan, PT. COME Indonusa Jakarta will expand the network not only for the Academy of Bina Sarana Informatika but also to the other universities. The selling order of the product of PT. COME Indonusa Jakarta using e-mail, sms, fax and telephone, the transaction processes are still done manually, so it needs longer time for the data to be recorded, processed or to be followed up. Using E-Commerce and considering the open market opportunity for selling the merchandise at campus, the writer intends to present the E-Commerce sites for campus merchandise using Unified Modeling Language (UML), which is expected can be utilized by customers. In addition it can expand the dissemination of information product and will certainly increase the profit at PT. COME Indonusa Jakarta. Intisari— E-Commerce (Electronic Commerce) telah menjadi trend dari bermunculannya ribuan perusahaan yang menjajakan dagangannya di dalam website. Persaingan yang semakin ketat, tentunya membuat perusahaan maupun individu berusaha menyediakan fasilitas kemudahan bagi konsumen atau user dalam menyusuri data-data barang sampai cara pemesanannya di dalam website, sehingga pada akhirnya konsumen atau user tersebut tertarik untuk membeli. PT. COME Indonusa Jakarta  merupakan perusahaan yang bergerak dalam bidang penjualan. Baik penjualan jasa dalam bisnis kursusnya, maupun penjualan barang khususnya produk-produk merchandise kampus. Adapun barang yang diproduksi sampai saat ini adalah merchandise kampus pada Akademi Bina Sarana Informatika. Dalam jangka panjang PT. COME Indonusa Jakarta akan merambah bisnis penjualan produk merchandise tidak hanya untuk kampus Akademi Bina Sarana Informatika saja, namun juga untuk kampus-kampus perguruan tinggi lainnya. Pemesanan penjualan merchandise kampus pada PT. COME Indonusa Jakarta yang masih menggunakan e-mail, sms, fax dan telepon serta transaksi penjualan yang masih dilakukan secara manual, terbilang lambat pada saat data itu dicatat, diproses atau dibutuhkan kembali. Dengan adanya E-Commerce dan melihat pasar yang masih sangat terbuka untuk produk merchandise kampus, penulis bermaksud menyajikan situs E-Commerce merchandise kampus dengan pemodelan sistemnya menggunakan Unified Modeling Language (UML), dimana diharapkan nantinya akan  lebih dimanfaatkan oleh lebih banyak pembeli. Selain itu dapat memperluas penyebaran informasi produk dan tentunya akan meningkatkan profit bagi PT. COME Indonusa Jakarta. Kata Kunci— E-Commerce, Merchandise Kampus, PT. COME Indonusa Jakarta, Unified Modeling Language (UML)
Dealing imbalance dataset problem in sentiment analysis of recession in Indonesia Kristiyanti, Dinar Ajeng; Sanjaya, Samuel Ady; Tjokro, Vinsencius Christio; Suhali, Jason
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2060-2072

Abstract

Global recession news dominates social media, particularly in Indonesia, with social news platforms on Twitter generating public responses and re-tweetings on the issue. Mining these opinions from Twitter using a sentiment analysis approach yields invaluable insights. The research stages included data collection, pre-processing, data labeling using the lexical-based method like valence aware dictionary and sentiment reasoner (VADER) and TextBlob, sampling techniques using synthetic minority oversampling technique (SMOTE) and random over sampling (ROS) before and after splitting data, and modeling using machine learning such as support vector machines (SVM), k-nearest neighbour (KNN), naive Bayes, and model evaluation. The problem is that almost 300,000 data collected from NodeXL are unbalanced. The findings show that models with balanced datasets show better model evaluation results. The sampling technique was carried out before and after splitting the data. The model evaluation results show that the Bernoulli-naive Bayes algorithm, with the VADER labeling technique, and the SMOTE sampling technique after splitting data, obtains the best accuracy of 84%, and using the ROS technique obtains an accuracy of 81%. On the other hand, with the SMOTE and ROS technique before splitting data on the SVM algorithm, it gets the best accuracy of 93% from before if only using SVM only reached 84%.
SISTEM PENDUKUNG KEPUTUSAN PEMBERIAN BONUS KARYAWAN MENGGUNAKAN METODE AHP PADA RUMAH SAKIT BUAH HATI CIPUTAT Stevanus, Rafhael; Handayani, Rani Irma; Kristiyanti, Dinar Ajeng
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 (1158.308 KB) | DOI: 10.33480/pilar.v14i2.78

Abstract

Dalam setiap perusahaan, instansi, organisasi atau badan usaha akan memberikan gaji sebagai kompensasi dari kinerja seorang karyawan. Hal ini dikarenakan karyawan merupakan salah satu sumber daya yang digunakan sebagai alat penggerak dalam memajukan suatu perusahaan. Disamping itu banyak perusahaan yang memberikan penghargaan kepada karyawannya melalui pemberian bonus berdasarkan kinerja karyawan yang dianggap memuaskan perusahaan dengan tujuan untuk memotivasi karyawan supaya dapat bekerja lebih giat lagi. Pada Rumah Sakit Buah Hati Ciputat sendiri perhitungan kriteria untuk penerimaan bonus karyawan masih secara subjektif atau perorangan. Sehingga membuat pemberian keputusan dalam pemberian bonus masih dirasa belum tepat, sedangkan dengan jumlah karyawan yang begitu banyak dapat memakan waktu yang relatif lebih lama. Setelah melakukan penelitian mengenai sistem pendukung keputusan pemberian bonus karyawan menggunakan metode AHP (Analytical Hierarchy Process), penulis telah menarik beberapa kesimpulan, diantaranya terdapat 4 kriteria perbandingan berpasangan yang digunakan Rumah Sakit Buah Hati Ciputat dalam proses pemberian bonus, yaitu: Keahlian, Sikap & Perilaku, Loyalitas, dan Tanggung Jawab.
PENERAPAN METODE WAITING LINE UNTUK EVALUASI PELAYANAN PENJUALAN MERCHANDISE KAMPUS PADA PT. COME INDONUSA JAKARTA Kristiyanti, Dinar Ajeng
Jurnal Pilar Nusa Mandiri Vol 14 No 1 (2018): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Maret 2
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1244.452 KB) | DOI: 10.33480/pilar.v14i1.91

Abstract

Dalam menjalankan bisnisnya, sebuah perusahaan dituntut untuk memberikan kualitas pelayanan yang terbaik bagi konsumen. Hal ini merupakan tanggung jawab bagi perusahaan dalam rangka menarik perhatian konsumen agar bisnis yang dijalani terus berkembang. Salah satu tolak ukur pelayanan yang baik adalah ketepatan waktu pelayanan, yang meliputi waktu mengantri dan waktu proses pelayanan. Antrian dapat saja timbul dikarenakan oleh kebutuhan akan layanan yang melebihi kapasitas layanan, sehingga pengguna fasilitas yang tiba tidak dapat segera mendapatkan layanan disebabkan oleh kesibukan layanan. Untuk itu dibutuhkan suatu metode untuk mengelola antrian tersebut sehingga fasilitas layanan yang ditambahkan tidak menimbulkan pengurangan keuntungan perusahaan. Dalam penelitian ini peneliti menggunakan metode Waiting Line untuk mengelola antrian pelayanan penjualan merchandise kampus yang masih belum optimal di PT. COME Indonusa Jakarta dengan cara menghitung sistem antrian perusahaan pada saat ini dan mengetahui sistem antrian perusahaan setelah dilakukannya alternatif solusi terpilih. Hasil penelitian menunjukkan dengan membuat sistem penjualan online (e-commerce), menambahkan staf operasional dan penambahan perangkat komputer mampu meminimalkan antrian dan mampu meningkatkan laba perusahaan.
DECISION SUPPORT SYSTEM IN DETERMINING THE BEST JUDO ATHLETE USING AHP METHOD Kristiyanti, Dinar Ajeng; Pangemanan, Garth Wishnuwardhana
Jurnal Pilar Nusa Mandiri Vol 16 No 1 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1157.424 KB) | DOI: 10.33480/pilar.v16i1.919

Abstract

To determine the best Judo athlete, Rajawali Judo Club of Battalion Arhanud 1 Divif 1 Kostrad has several obstacles such as making a decision in determining the best Judo athlete by the Coach and the Achievement Development which only based on experience which is estimated without the existence of any system. This results in subjectivity and the absence of a strong basis for competent objective decision making which then triggers gaps between athletes. The absence of specific criteria creates that result in not targeting the selection of the best Judo athletes. For this reason, a method, in this case, is needed to choose the AHP (Analytical Hierarchy Process) method and a number of criteria as indicators in determining the best Judo athlete. While the referenced criteria are Self-Dropping Technique (Ukemi), Slamming Technique (Nage-waza), Lockdown or Lying Technique (Katame-waza), Discipline and Achievement. The purpose of this study is expected to produce statistical data as an evaluation material for the training team to minimize or even eliminate the gap between fellow Judo athletes at the Rajawali Judo Club of Battalion Arhanud 1 Divif 1 Kostrad. The result of this study is based on Analytical Hierarchy Process calculations, obtained the most important priority criteria in determining the best Judo athlete in which the Achievement criteria with value 0.325 or equivalent to 32%, then followed by Disciplinary criteria with value 0.227 or equivalent to 23%, Slamming Technique criteria (Nage-waza) with value 0.211 or equivalent to 21%, Lockdown/Laying Technique criteria (Katame-waza) with value 0.125 or equivalent to 12% and in the last rank the Self-Dropping Technique criteria (Ukemi) with value 0.112 or equivalent to 11%.
Pemilihan Karyawan Terbaik Menggunakan Metode Simple Additive Weighting Berbasis Sistem Pendukung Keputusan Kristiyanti, Dinar Ajeng; Sayoeti, Natanael
Jurnal Informasi dan Teknologi 2022, Vol. 4, No. 2
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v4i2.196

Abstract

One alternative in making effective decisions is the Decision Support System (DSS). The selection of the best employees still has weaknesses in PT. Petromitra Pacific Internusa so that it will trigger conflicts between employees. In this study, the Simple Additive Weighting (SAW) method was used to process employee data in all divisions to obtain accurate results. The SAW method is one of the methods that can assist in the decision-making process for selecting the best 100 employees by providing criteria and preference weights that can be determined according to applicable regulations such as 35% discipline criteria, 15% cooperation, 45% leadership, and honesty. 10%. This study aims to provide more accurate results and make it easier for the Personnel or HRD department to choose the best employees at PT. Petromitra Pacific Internusa. The results of this study are in the form of determining the selection of the best employees who are identified based on the results of the highest score. So that they will automatically become the best employees and will receive bonuses, as well as get a promotion.
Optimizing long short-term memory hyperparameter for cryptocurrency sentiment analysis with swarm intelligence algorithms Ekachandra, Kristian; Kristiyanti, Dinar Ajeng
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2753-2764

Abstract

This study investigates the application of swarm intelligence algorithms, specifically particle swarm optimization (PSO), ant colony optimization (ACO), and cat swarm optimization (CSO), to optimize long short-term memory (LSTM) networks for sentiment analysis in the context of cryptocurrency. By leveraging these optimization techniques, we aimed to enhance both the accuracy and computational efficiency of LSTM models by fine-tuning critical hyperparameters, notably the number of LSTM units. The study involved a comparative analysis of LSTM models optimized with each algorithm, evaluating performance metrics such as accuracy, loss, and execution time. Results indicate that the PSO-LSTM model achieved the highest accuracy at 86.08% and the lowest loss at 0.57, with a reduced execution time of 58.43 seconds, outperforming both ACO-LSTM and CSO-LSTM configurations. These findings underscore the effectiveness of PSO in tuning LSTM parameters and emphasize the potential of swarm intelligence for enhancing neural network performance in real-time sentiment analysis applications. This research contributes to advancing optimized deep learning techniques in high dimensional data environments, with implications for improving cryptocurrency sentiment predictions.
Enhancing Heart Disease Classification: A Comparative Analysis of SMOTE and Naïve Bayes on Imbalanced Data Wibowo, Jonathan Juliano; Kristiyanti, Dinar Ajeng; Wiratama, Jansen
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3248

Abstract

Heart disease remains a significant health concern, and early prediction plays a crucial role in improving patient outcomes. This study examines data mining techniques for heart disease classification, with a focus on the Naïve Bayes algorithm. A common challenge in such classification tasks is data imbalance, which can negatively impact the performance and evaluation metrics of the algorithm. To address this, we employed the Synthetic Minority Over-sampling Technique (SMOTE) to handle imbalanced data. Using the Knowledge Discovery in Databases (KDD) framework, the research followed data selection, pre-processing, transformation, mining, and evaluation stages. We applied SMOTE to the Naïve Bayes algorithm across three data split ratios (70:30, 60:40, and 50:50) and compared performance metrics before and after the SMOTE application. For the first dataset, the 50:50 split ratio showed the most tremendous improvement, with precision increasing from 30.74% to 78.15%, recall from 42.88% to 63.89%, and the Area Under Curve (AUC) from 0.819 to 0.831, although accuracy decreased from 86.82% to 73.01%. For the second dataset, the 70:30 split ratio yielded the most significant improvements, with accuracy rising from 95.22% to 97.72%, precision from 96.33% to 99.88%, recall from 51.11% to 95.57%, and AUC from 0.969 to 0.996. These results demonstrate that SMOTE can substantially improve classification performance in heart disease prediction, particularly in precision, recall, and AUC, with varying effects on accuracy depending on the dataset.
Comparison of Salp Swarm Algorithm and Particle Swarm Optimization as Feature Selection Techniques for Recession Sentiment Analysis in Indonesia Kristiyanti, Dinar Ajeng; Sanjaya, Samuel Ady; Irmawati, Irmawati; Ekachandra, Kristian; Suhali, Jason; Hairul Umam, Akhmad
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3102

Abstract

Amidst global economic uncertainty, this study focuses on Twitter sentiment during the global recession issue on social media, especially in Indonesia. By utilizing sentiment analysis, this study uses machine learning algorithms such as Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) which are still less than optimal on high-dimensional Twitter data. The purpose of this study is to improve the accuracy of conventional machine learning using basic metaheuristic algorithms, namely the Salp Swarm Algorithm (SSA) and Particle Swarm Optimization (PSO) as feature selection. From January to May 2023, this study captures the evolving sentiment in response to economic conditions. Data preprocessing, including labeling through the TextBlob and VADER libraries, sets the stage for the analysis. Performance is compared based on labeling techniques, feature selection, and classification algorithms. Specifically, when applied to VADER labeled data without feature selection, the SVM model achieves an outstanding accuracy of 83% and an F1 score of 67%—notably, the application of SSA and PSO results in a reduction in model accuracy by 1%. However, the application of SSA and PSO slightly reduced the model accuracy performance by 1%. On the TextBlob labeled data, SVM showed an outstanding performance (80% accuracy, 77% F1 score). Interestingly, PSO on TextBlob data with SVM significantly decreased the model's performance. These findings contribute significantly to understanding the intricacies of sentiment dynamics during economic uncertainty on social media platforms, with SVM emerging as a strong choice for practical sentiment analysis.
Digitalization of village based on information technology through developing BUMDes MSMEs website and logo Kristiyanti, Dinar Ajeng; Alexandra, Yoanita; Situmorang, Ringkar; Athira, Reva Fakhrana; William, Juanito Arvin
Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Vol 7 No 1 (2024)
Publisher : University of Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jipemas.v7i1.20803

Abstract

The development of technology has transformed the global economic landscape, including in the realm of Micro, Small, and Medium Enterprises (MSMEs). However, MSME actors in various villages and regions often still have limitations in understanding the importance of digitalization, especially in utilizing technology to promote their products. Therefore, this Community Engagement activity is initiated with the main goal of creating and disseminating understanding about the importance of implementing a profile website, financial management application, and logo in efforts to increase visibility and sales of products for MSME actors in the Serdang Tirta Kencana Village-Owned Enterprises (BUMDes) in Tangerang, Indonesia. The implementation method is based on Community-Based Participatory Research Program (CBPR) with the following stages are location survey, website and logo creation, socialization, and evaluation. Through close collaboration with local stakeholders from BUMDes Serdang Tirta Kencana, this activity has successfully empowered MSME actors with a strong visual identity and significant digital presence. The result is a 95% increase in the skills of MSME actors in BUMDes Serdang Tirta Kencana. It is hoped that through this activity, MSME actors can become competitive and have a positive impact on local economic growth and community empowerment
Co-Authors Alexandra, Yoanita Angelin, Angelin Athira, Reva Fakhrana Demi, Sita Winiawati Dwiza Riana Ekachandra, Kristian Evi Sopandi, Evi Hadinata, Faustine Ilone Hairul Umam, Akhmad Hidayat, Wanda Gema Prasadio Akbar Irmawati Irmawati JARWADI, JARWADI Karaeng, Cristin Natalia Kurniawan, Paulus Heru Wibowo Pangemanan, Garth Wishnuwardhana Primadini, Intan Rani Irma Handayani, Rani Irma Rosdiana, Tjaturningsih Sanjaya, Samuel Ady Sayoeti, Natanael Situmorang, Ringkar Sri Hadianti Stevanus, Rafhael Suhali, Jason Takasili, Tevin Tjokro, Vinsencius Christio Tohir, Herdian Wibowo, Jonathan Juliano William, Juanito Arvin Willona, Yemima Wiratama, Jansen