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All Journal Jurnal INSYPRO (Information System and Processing) SINTECH (Science and Information Technology) Journal Jurnal Informatika Universitas Pamulang ILKOM Jurnal Ilmiah Kharisma Tech Journal of Technology Research in Information System and Engineering International Journal of Artificial Intelligence in Medical Issues

Husni Angriani

STMIK KHARISMA Makassar

Author-ID : 2655864

Computer Science & IT Control & Systems Engineering Dentistry Electrical & Electronics Engineering Engineering Health Professions Medicine & Pharmacology Public Health

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Articles

Rancang Bangun Sistem e-Voting Pemilihan Ketua Organisasi Kemahasiswaan Menggunakan Metode Rapid Application Development Husni Angriani; Yeni Saharaeni
Jurnal INSYPRO (Information System and Processing) Vol 5 No 1 (2020)
Publisher : Prodi Sistem Informasi UIN Alauddin

Tujuan dari penelian ini yaitu membangun sebuah sistem pemilihan ketua organisasi dengan memanfaatkan sistem e-voting yang memudahkan mahasiswa dalam melakukan pemilihan ketua organisasi. Metode yang digunakan dalam merancang dan membangun sistem ini adalah Rapid Application Development. Studi kasus penelitian ini adalah proses pemilihan ketua organisasi kemahasiswaan pada unit kegiatan mahasiswa (UKM) kampus STMIK KHARISMA Makassar. Hasil dari penelitian ini menunjukkan bahwa penerapan e-voting dapat memudahkan mahasiswa melakukan pemilihan ketua organisasi tanpa terkendala tempat dan waktu, serta pemanfaatan e-voting tersebut dapat mengefisienkan biaya voting.

IMPLEMENTASI TEORI NAIVE BAYES DALAM KLASIFIKASI CALON MAHASISWA BARU STMIK KHARISMA MAKASSAR Irayori Loelianto; Moh. Sofyan S Thayf; Husni Angriani
SINTECH (Science and Information Technology) Journal Vol. 3 No. 2 (2020): SINTECH Journal Edition Oktober 2020
Publisher : LPPM STMIK STIKOM Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v3i2.651

STMIK KHARISMA Makassar has graduated thousands of alumni since it was founded. However, the number of students registering is uncertain every year, although from 2016 to 2019 there has been an increase in the number of registrations. The problem is the percentage of the number of prospective students registering has actually decreased significantly. The purpose of this research is to implement the Naive Bayes theory in classification of STMIK KHARISMA Makassar prospective students. This research basically uses the Naive Bayes theory as a classifier, and is made using the Python programming language. At the classifier design stage, there were a total of 499 data collected from 2016 to 2019. The data was divided by a ratio of 80:20 for training data and test data. The result from the research indicate the level of accuracy of the classifier reaches 73%.

Implementasi Teori Naive Bayes dalam Klasifikasi Ujaran Kebencian di Facebook Willianto Willianto; Izmy Alwiah Musdar; Junaedy Junaedy; Husni Angriani
Jurnal Informatika Universitas Pamulang Vol 6, No 4 (2021): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v6i4.12593

Hate Speech can be orally or in writing which is expressed intentionally by someone for the purpose of spreading and leading to hatred between groups of people. The phenomenon of Hate Speech has become a hot topic. This is motivated by netizens who often express Hate Speech either in the comments column or in their personal status on social media. The impact of this phenomenon is the emergence of hatred in society which can lead to conflict. The purpose of this study is to implement the Naïve Bayes Theory in the classification of Hate Speech on Facebook. In this study Naïve Bayes is used as a Classfifier. Naïve Bayes method is applied to find the probability of words in documents would be categorized as hate speech or not hate speach. This Classfifier is implemented using Python programming language. In the Classfifier design stage, 500 data are collected randomly on Facebook. Data is divided by 80% - 20% , 400 text data for training and 100 text data for testing. The accuracy for hate speech classification in this study is 83%. These results are obtained from Classfifier evaluations using test data where the Classfifier correctly labels 83 out of 100 test data.

IMPLEMENTASI TEKNOLOGI RFID PADA SISTEM ANTRIAN REKAM MEDIS PASIEN DI RUMAH SAKIT Musfirah Putri Lukman; Husni Angriani
ILKOM Jurnal Ilmiah Vol 10, No 1 (2018)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v10i1.246.105-112

Penelitian ini bertujuan untuk mengurangi waktu antri pasien ketika memproses rekam medis pada pendaftaran rawat jalan di rumah sakit. Untuk tujuan tersebut, maka diusulkan pemanfaatan Radio Frequency Identification (RFID) dimana RFID ini berguna sebagai kode unik pasien. Kode unik tersebut akan menampilkan data pasien secara otomatis sehingga tidak memerlukan waktu pencarian berkas pasien yang dapat menambah waktu antrian pasien. Teknik perancangan sistem pada penelitian ini menggunakan metode prototyping sedangkan teknik pengujian analisis data menggunakan metode analisis uji-T. Pengujian perangkat lunak menggunakan teknik pengujian black box. Hasil penelitian yang diperoleh adalah sistem mampu mempercepat proses antrian rekam medis pasien pada rumah sakit dengan waktu rata - rata sebesar 3.6 menit dengan selisih waktu 9.4 menit dari waktu rata - rata antrian untuk sistem konvensional sebesar 12.6 menit. Secara keseluruhan dapat disimpulkan bahwa rata-rata waktu antri sistem RFID lebih singkat daripada waktu antri sistem konvensional sehingga sistem RFID mampu mempersingkat waktu antri pasien untuk proses rekam medis.

PENERAPAN METODE FULL COSTING PADA SISTEM INFORMASI HARGA POKOK PRODUKSI PADA DAHAN KONVEKSI Bryan Samuel Bangonan; Afifah; Husni Angriani
KHARISMA Tech Vol 14 No 1 (2019): Jurnal KHARISMA Tech
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (306.878 KB)

Base cost of production is very important on counting profit and loss of the company. If the company is not thorough or incorrect in counting the cost of production, it can lead to error in determining the profit and loss that the company obtained. It is very important to correctly determine the base cost of production. The tight competition in the market forces one company to compete with another in producing product or service, which leads to the importance of obtaining information regarding the cost and the base cost of production to help make the right decision. This research is intended to provide a computerized system that can help Dahan Konveksi in determining the base cost of production to decide on the cost of goods sold using the Full Costing method. Based on the production cost on December, it was determined that the base cost of production will be Rp 59.435. From that result, it was decided that the target selling price will be Rp 95.096. Using the Full Costing method, where fixed cost and variable cost are excluded, selling price per sheet is obtained. It can also be a basic cost in determining the selling price target.

IMPLEMENTASI SEO (SEARCH ENGINE OPTIMIZATION) ON PAGE UNTUK MENINGKATKAN PENGUNJUNG WEBSITE XTRAORDINARY Adrian Suwardi; Husni Angriani; Afifah
KHARISMA Tech Vol 17 No 1 (2022): Jurnal KHARISMATEch
Publisher : STMIK KHARISMA Makassar

Private course is an informal institution that allows students to get additional lesson in order to enhance theirs understanding on certain subject. Most parents rely on professional services to help their children in learning. Xtraordinary Website is one of websites that provides private tutors which is can be accessed through page https://www.xtraordinarytutor.com. According to the results of Google Analytics monitoring that the website since its first launch, it has not had visitors. The implementation of SEO On Page is carried to increase the number of visitors. The implementation of SEO On page includes Title Tags, Meta Descriptions, Meta Keywords, Meta Headings, and Meta Images. The result indicated the effectiveness of SEO On Page in achieving faster result in short time. Increase in visitors’ traffic on Xtraordinary website is showed by the implementation of SEO On Page. As the effect of implementation also placed the Xtraordinary website as first rank on search engine with keywords related to private tutors.

SISTEM MANAJEMEN PERSEDIAAN BARANG PADA RETAILER MENGGUNAKAN METODE SINGLE EXPONENTIAL SMOOTHING Husni Angriani
JTRISTE Vol 4 No 1 (2017)
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (371.035 KB)

Determining the amount of stock for the right inventory becomes an issue in meeting the number of consumer needs for retailers. The problem affects the process of selling goods for retailers to be hampered. This research is developed a model of determining the amount of inventory on a retailer by using a single exponential smoothing method to forecast quantity order or how many items should be in the message to meet the end consumer demand.The results of this research indicate that the system developed can make the determination of the amount of goods to be ordered by retailers in meeting customer needs. Testing this system using sales data conducted during a month that is implemented into the system. The result of data entered into the system is shown that the retailer's inventory cost is too large. This is because too much inventory on each side is not in accordance with consumer demand, so it can cause harm to all parties. The developed system can determine the exact amount of goods ordering on retailers with single exponential smoothing method.

Implementasi Metode Decision Tree Dalam Menentukan Pemberian Kredit Mobil Menggunakan Visual Basic (Studi Kasus UD PUTRA MAS Makassar) Junaedy; Izmy Alwiah Musdar; Husni Angriani
JTRISTE Vol 4 No 2 (2017)
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (309.781 KB)

UD Putra Mas is developing its business by granting credit on automobile purchases. The criteria of calculation used to determine the decision on credit grant is based on the amount of salary, saving, expense, credit count, and a complete set of documents. The process itself meets a couple of obstacles, such as the piling stacks of debtors’ archives in the cabinet and the difficulty in data searching. The purpose of this research is to overcome the obstacles mentioned above. The application used to develop the system is Visual Basic 6.0 and Microsoft Access 2003 as the data management media. The system created is a Decision Support System using Decision Tree Method to obtain the model. The research is based on the result of an interview with the head of UD Putra Mas to collect significant information required by the system. The output of this system is alternative of choices. This research results in a Decision Support System for Credit Grant which helps the head of UD Putra Mas in making decisions.

Implementasi Metode Prototype pada Rancang Bangun Sistem Pendukung Keputusan Pemilihan Mahasiswa Berprestasi Berbasis Web Husni Angriani; Yeni Saharaeni; Hasniati Hasniati
Jurnal INSYPRO (Information System and Processing) Vol 8 No 1 (2023)
Publisher : Prodi Sistem Informasi UIN Alauddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/insypro.v8i1.36281

The selection of outstanding students can be an important factor in awarding scholarships at the university level. However, in some cases, the process of selecting outstanding students and awarding scholarships is considered to be less objective, resulting in scholarships being awarded to students who may not necessarily be the most deserving. To address this issue, a system for selecting outstanding students was developed at STMIK KHARISMA Makassar to help all stakeholders in the institution make a more objective selection. The system is web-based and can be accessed in real-time by all stakeholders. The prototype method was used to help stakeholders and software engineers describe the software's requirements and specifications. The implementation of the system showed that it can assist in the evaluation process objectively based on the criteria set by all stakeholders, and that all system specifications are relevant to the user requirements. In conclusion, the development of this system has the potential to improve the selection process for outstanding students and ensure that scholarships are awarded to the most deserving students. By providing a more objective and transparent process, this system can help ensure that awards and scholarships are awarded on target.

Optimizing Neurodegenerative Disease Classification with Canny Segmentation and Voting Classifier: An Imbalanced Dataset Study A. Sinra; Bagus Satrio Waluyo Poetro; Husni Angriani; Hamada Zein; Izmy Alwiah Musdar; Medi Taruk
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 2 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i2.97

This study explores the efficacy of a Voting Classifier, combining Logistic Regression, Random Forest, and Gaussian Naive Bayes, in the classification of neurodegenerative diseases, focusing on Alzheimer's Disease (AD), Parkinson’s Disease (PD), and control groups. Utilizing a dataset pre-processed with Canny segmentation and Hu Moments feature extraction, the research aimed to address the challenges posed by imbalanced datasets in medical image classification. The classifier's performance was evaluated through a 5-fold cross-validation approach, with metrics including accuracy, precision, recall, and F1-Score. The results revealed a consistent recall rate of approximately 46% across all folds, indicating the model's effectiveness in identifying cases of neurodegenerative diseases. However, the precision and F1-Score were notably lower, averaging around 22% and 29%, respectively, underscoring the difficulties in achieving accurate classification in imbalanced datasets. The study contributes to the understanding of machine learning applications in medical diagnostics, specifically in the challenging context of neurodegenerative disease classification. It highlights the potential of using advanced image processing techniques combined with machine learning ensembles in enhancing diagnostic accuracy. However, it also draws attention to the inherent challenges in such approaches, particularly regarding precision in imbalanced datasets. Recommendations for future research include exploring data balancing techniques, alternative feature extraction methods, and different machine learning algorithms to improve the precision and overall performance. Additionally, applying the model to a broader and more diverse dataset could provide more generalizable and robust findings. This study is significant for researchers and practitioners in medical imaging and machine learning, offering insights into the complexities and potential of automated disease classification

Co-Authors A. Sinra Adrian Suwardi Afifah Bagus Satrio Waluyo Poetro Bryan Samuel Bangonan Hamada Zein Hasniati Hasniati Irayori Loelianto Izmy Alwiah Musdar izmy alwiah musdar Junaedy Mohammad Sofyan S. Thayf Musfirah Putri Lukman Taruk, Medi Willianto Willianto Yeni Saharaeni Yeni Saharaeni

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