1,721,026 research outputs found
COBIT 5 Capability Level of Information Technology Governance at PT ABC
Full text linkIn Indonesia, PT ABC is an ICT business
that specializes in network services, IT management,
and system integrators. The company's IT governance
mechanisms are still not working to their full potential.
This is demonstrated by the inability of PT ABC to
achieve its business objectives due to poor management
of its IT resources, difficulties in addressing issues, and a
failure to comply with external obligations. Utilizing the
COBIT 5 framework and an IS audit phase, the
capability level measurement and gap analysis for IT
governance at PT ABC were conducted to address all of
these issues. Purposive sampling and a qualitative
research methodology were both used in this study.
Interviews and observations served as the major and
secondary data sources. As a result, capability level
measurement and gap analysis at PT ABC stop at level 1
for the EDM04 process and have a gap value of 1,
whereas they stop at level 3 for the DSS03 and MEA03
processes and have no gap value. Therefore, in order for
the IT application to be more optimal and to prevent or
reduce the recurrence of the same issues in the future,
PT ABC needs to implement and follow up on the advice
made
Exploring the Role of Machine Learning and Big Data Analytics in Enhancing Decision-Making Processes: A Systematic Literature Review
Full text linkThis Systematic Literature Review (SLR) analyzes the influence of Machine Learning (ML) and Big Data Analytics (BDA)
on decision-making processes in several industries. The study aims to explore the potential of machine learning and big data analytics
in enhancing decision-making, examining the tools and platforms used, and identifying the challenges encountered during deployment.
Employing the PRISMA technique, 31 publications published from 2019 to 2024 were meticulously selected through a stringent
screening process, using Scopus as the principal database. The results indicate that machine learning and big data analytics substantially
enhance predictive accuracy, operational efficiency, and data privacy measures, while facilitating seamless integration with current
systems. Furthermore, these technologies are becoming progressively accessible to Small and Medium Enterprises (SMEs). In the
healthcare sector, machine learning models have exhibited a diagnosis accuracy of 99% in detecting breast cancer. Nonetheless, the
report underscores other research deficiencies, particularly the necessity for more cost-effective solutions designed for SMEs. These
limitations signify opportunities for future study to investigate ML and BDA applications in underexamined areas, such as logistics and
manufacturing. This research highlights the necessity of creating economical, scalable, and industry-specific machine learning and big
data analytics solutions to address existing difficulties. This systematic literature review (SLR) seeks to elucidate the function of machine
learning (ML) and big data analytics (BDA) in decision-making, thereby assisting researchers and practitioners in enhancing the
utilization of these technologies across many industrial applications
Information Technology Capability Using COBIT 2019 Framework (Case Study: PT. Emobile Indonesia)
Full text linkPT. Emobile Indonesia is an Information
Technology company that manages services and supports several
sectors of the business industry. This assessment is done because
the governance in the company is still not integrated properly.
This is indicated by the irregular role and description of tasks
within the IT division, to slow down performance when sudden
disruptions occur and lack of implementation of IT Risk in the
company. To overcome these constraints, an assessment of
Information Technology governance with COBIT 2019 method is
carried out and assisted by descriptive methods and supported by
quantitative data. The initial stages are conducting interviews to
employees at PT. Emobile Indonesia for data collection. The
domains that are followed up from COBIT 2019 are EDM03 -
Ensured Risk Optimization, APO01 - Managed I&T
Management Framework, and APO07 - Managed Human
Resources. The results obtained after measuring IT capabilities
in APO01 and APO07 domain companies stopped at level 2 that
did not meet the company's target and only EDM03 met the
company's target of level 3. The results of the study were used to
determine the level of governance of information technology in
each domain. As well as helping the company fix the obstacles
that occur by providing recommendations to PT. Emobile
Indonesia
FAKTOR-FAKTOR PENYEBAB PERMUKIMAN KUMUH PADA KAWASAN PARIWISATA DI DESA GILI INDAH KECAMATAN PEMENANG KABUPATEN LOMBOK UTARA
Full text linkTujuan dari penelitian ini adalah untuk mengetahui Faktor-Faktor Penyebab Permukiman Kumuh Pada Kawasan Pariwisata Di Desa Gili Indah Kecamatan Pemenang Kabupaten Lombok Utara, yang menggunakan metode penelitian adalah Metode penelitian pendekatan deskriptif kuantitatif. Dengan teknik analisis data yang digunakan dalam penelitian ini menggunakan analisis penentuan lokasi permukiman kumuh berdasarkan Peraturan Menteri Pekerjaan Umum Dan Perumahan Rakyat Republik Indonesia Nomor 14/PRT/M/2018 Tentang Pencegahan Dan Peningkatan Kualitas Terhadap Perumahan Kumuh Dan Permukiman Kumuh. Adapun hasil penelitian yakni Dusun Gili Air merupakan dusun dengan kepadatan bangunan tertinggi yaitu 12,1 unit/Ha, sedangkan kepadatan terendah adalah Dusun Gili Meno dengan kepadatan bangunan 8,8 unit/Ha. Serta Kondisi bangunan gedung pada perumahan dan permukiman di Desa Gili Indah yang tidak sesuai dengan persyaratan teknis, tata bangunan, dan keandalan bangunan dengan persyaratan berdasarkan data permukiman kumuh terdapat 38 unit di Dusun Gili Air, 12 unit di Gili Meno dan 67 Unit Di Gili Trawangan dengan berbagai pertimbangan yakni bangunan yang peruntukan lokasi dan intensitas bangunan gedung; arsitektur bangunan gedung; pengendalian dampak lingkungan; rencana tata bangunan dan lingkungan (RTBL); dan pembangunan bangunan gedung di atas dan di bawah tanah,air dan/atau Prasarana/Sarana umum. Untuk kondisi air minum di kawasan permukiman kumuh Desa Gili Indah sebagian besar penduduknya masih menggunakan sumur bor yang di proses menggunakan suling. Dan ada beberapa lingkungan di Dusun Gili Air yang hampir semuanya sudah terlayani oleh PDAM yaitu sekitar 90% dari jumlah KK yang ada
Audit Sistem Informasi Menggunakan Cobit 5.0 Domain DSS pada PT Erajaya Swasembada, Tbk
No full textThe object of research is PT Erajaya Swasembada, Tbk. The company was founded in 1990, running a business as a distributor of mobile phones, which has outlets in various cities in Indonesia. Studied business processes includes sales, purchasing, finance, and warehouse, ERP (Enterprise Resource Planning). The research was conducting an audit of information technology governance at PT Erajaya Swasembada, Tbk, which is more focused on users who are involved in the cycle of the company’s Enterprise Resource Planning use. Auditing information technology governance using the COBIT 5.0 framework, focusing on the domain Deliver, Service, and Support (DSS). The results obtained from this study is any process that exist in the domain Deliver, Service, and Support (DSS) is in level 3 (established process) and 4 (predictable process) capability models. For DSS01, DSS02 and DSS06 is at level 4, while for the DSS03, DSS04 and DSS05 is at level 3. The company has already implemented the service and support of information technology governance is well proven from their operational procedures in the provision of services to internal and external, incident handling procedures, and maintenance control of appropriate business processes. Service and support of information technology remains to be improved in a sustainable manner
Enhancing MySQL Database Security with MySQL Enterprise Transparent Data Encryption
Full text linkWith the increasing threats to data security and the potential consequences of data
breaches, the demand for data security has steadily risen. Protecting sensitive information,
particularly stored in databases, has become a crucial aspect of data management. Considering
this, this study focuses on a specific solution for data protection within databases, MySQL
Transparent Data Encryption, specifically in the case of PT ABC, an information and
technology company. MySQL Transparent Data Encryption was implemented as a potential
solution to enhance data security. The implementation of MySQL Transparent Data
Encryption was tested by benchmarking to evaluate the effectiveness and efficiency of the
solution before and after the implementation using JMeter, a widely recognized and reliable
tool for performance testing. The research findings demonstrate that implementing MySQL
Transparent Data Encryption could effectively secure the database while having a minimal
impact on performance. This is a significant finding, as it shows that enhanced data security
only sometimes comes at the cost of reduced performance. The encryption implementation
resulted in a less than 10% decrease in database performance, indicating that the advantages
of securing data outweigh the minor performance decrease. In conclusion, this study confirms
the reliability of MySQL Transparent Data Encryption as a solution for securing data within
a MySQL database. It confirms that this method of encryption is effective in enhancing data
security and efficient in maintaining the performance of the databas
A Comparative Study of Machine Learning Approaches to Megathrust Earthquake Prediction in Subduction Zones
Full text linkMegathrust earthquakes are one of the most severe threats to countries situated along tectonic subduction zones, particularly Indonesia, where the movement of converging plates frequently triggers large-scale seismic events and tsunamis. Although recent developments in seismology have introduced various predictive tools, many of these models still face challenges, especially due to limitations in hydrogeological data quality. This study aims to investigate how three different machine learning algorithms perform in predicting megathrust earthquake events. The algorithms tested are Support Vector Machine, Random Forest, and Artificial Neural Network, applied to a dataset dominated by earthquake records from the Indonesian and Pacific regions. Each model was evaluated based on accuracy, precision, recall, and F1 score to provide a comprehensive performance analysis. The results show that Random Forest produced the highest accuracy, reaching 96%, followed closely by Support Vector Machine with 95%, while Artificial Neural Network achieved 83%. In terms of the F1 score, Random Forest led with a score of 0.95, indicating balanced performance in classification. However, recall, which is critical in disaster preparedness because it measures the model’s ability to detect high-risk events, Artificial Neural Network reached 92% for tsunami-related classifications. This suggests that while Random Forest is the most accurate overall, Artificial Neural Network could be more appropriate for early warning systems where the cost of missing a true event is much higher than issuing a false alarm. The contribution of this research is the direct comparison of multiple machine learning methods using real earthquake data, focusing not only on accuracy but also on practical disaster management considerations such as recall. This study also presents a novel perspective by analyzing the trade-off between model accuracy and disaster risk, emphasizing the need for probabilistic forecasts that can support timely public decision-making during seismic crises
Analisis Proses Bisnis ERP dan Pengawasan terhadap Implementasi Software Point of Sales Azec pada PT Erajaya Swasembada, Tbk
Full text linkPerkembangan teknologi dan ilmu pengetahuan yang cepat semakin
menuntut pemakaian teknologi tersebut ke dunia kerja untuk memudahkan dalam
menyelesaikan pekerjaan. Hal ini membuat banyak perusahaan besar maupun
perusahaan-perusahaan menengah ke bawah menerapkan teknologi di dalam
proses bisnis mereka, misalnya dengan mengimplementasikan suatu sistem yang
bekerja di dalam proses bisnis perusahaan mereka.
Perusahaan yang dijadikan sebagai tempat penulis untuk menimba
pengalaman adalah PT Erajaya Swasembada, Tbk. Perusahaan ini merupakan
suatu perusahaan yang berdiri sejak tahun 1990, menjalankan bisnis sebagai
distributor telepon genggam, yang memiliki outlet-outlet tersebar luas pada
berbagai kota di wilayah Indonesia. Pada perusahaan ini sudah menjadi lumrah
dalam pemakaian sistem untuk membantu berjalannya alur proses bisnis yang
baik. Proses bisnis yang dimiliki mencakup penjualan, pembelian, bagian
keuangan, dan bagian gudang.
Sistem yang digunakan pada tiap-tiap outlet juga harus menunjang
kemudahan penginputan data serta output laporan yang mudah dipahami oleh
seluruh karyawan yang terkait. Pada outlet yang dimiliki perusahaan ini memakai
software Point of Sales Azec. Software ini penting dalam membantu
mengorganisir data mengenai penjualan outlet tiap harinya. Software POS Azec
ini menggantikan POS Race sebagai software terdahulu yang dipakai pada outlet
iBox. Penggantian software ini dikarenakan tuntutan basis data yang dimiliki oleh
PT Erajaya Swasembada, Tbk yang berbasis Oracle, sedangkan POS Race hanya
mampu dikoneksikan dengan MySql. Sejak 1 Agustus 2012 POS Azec resmi
dipakai pada seluruh outlet iBox di Jabodetabek.
Untuk meminimalisasikan seluruh risiko tersebut, maka diperlukan suatu
pengawasan sistem yang sudah paham betul akan sistem baru tersebut serta siap
setiap saat dalam penanganan error juga menghadapi kebingungan para karyawan
outlet. Dalam pengawasan tersebut didapat temuan-temuan kendala antara lain:
kesalahan (error) yang sering terjadi dikarenakan database yang belum stabil;
masih terdapat kesalahan (error) yang terjadi pada saat penggunaan sistem ini;
masih terdapat kesalahan penghitungan pada laporan. Oleh karena itu, penulis
selaku pengawas sistem menyimpulkan bahwa sistem yang baru dinyatakan
belum siap untuk digunakan live pada outlet-outlet perusahaan ini
Studi Kualitas Audit Sistem Informasi di Indonesia Berdasarkan Kompetensi dan Independensi Auditor
No full textComparative Evaluation of CNN-LSTM Model for Emotion Detection in Indonesian Text
Full text linkThis study developed an emotion detection model for Indonesian text using a
dataset from prior research. The data was refined through multiple pre-processing steps before
applying CNN-LSTM machine learning techniques. Comparative analysis indicated the
model achieved 58% accuracy, lower than baseline methods. The results imply need for larger
annotated corpora, improved text normalization, and integration with state-of-the-art deep
learning approaches to enhance performance for Indonesian emotion detection
- …
