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Diagonal Based Feature Extraction and Backpropagation Neural Network in Handwritten Batak Toba Characters Recognition Zamzami, Elviawaty Muisa; Hayanti, Septi; Nababan, Erna Budhiarti
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 2, May 2021
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v6i2.1212

Handwritten character recognition is considered a complex problem since one’s handwritten character has its characteristics. Data used for this research was a photo of handwritten or scanned handwritten. In this research, Backpropagation Neural Network (BPNN) was used to recognize handwritten Batak Toba character, wherein preprocessing stage feature extraction was done using Diagonal Based Feature Extraction (DBFE) to obtain feature value. Furthermore, the feature value will be used as an input to BPNN. The total number of data used was190 data, where 114 data was used for the training process and another 76 data was used for testing. From the testing process carried out, the accuracy obtained was 87,19 %.

ONTOLOGI PADA METODE REQUIREMENTS RECOVERY DALAM PROSES REVERSE ENGINEERING Elviawaty Muisa Zamzami; Eko Kuswardono Budiardjo
Semantik Vol 1, No 1 (2011): Prosiding Semantik 2011
Publisher : Semantik

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (403.822 KB)

Piranti lunak kerap mengalami perubahan, antara lain disebabkan perubahan bisnis organisasi. Akibatnya, peranti lunak tidak mampu lagi mendukung organisasi sehingga harus dilakukan reengineering terhadapnya. Software reengineering didukung antara lain oleh riset reverse engineering. Riset reverse engineering saat ini lebih terfokus pada pengembangan software tools dan perolehan kembali model fisik dan struktur lojik dalam bentuk model-model dari legacy system [1] . Namun, riset untuk requirements recovery melalui proses reverse engineering masih relatif minim. Disisi lain, requirements sangat penting bagi keberhasilan software reengineering. Dengan demikian, terdapat kepentingan melakukan riset reverse engineering untuk requirements recovery dari peranti lunak yang ada (existing software). Requirements recovery dari peranti lunak dapat memastikan pemahaman lebih baik dari apa yang redundan, apa yangharus dipertahankan, dan apa yang dapat digunakan kembali [1]. Requirements yang diperoleh dapat digunakan pada forward engineering sebagai bagian dari reengineering ataupun menyusun ulang dokumen existing requirements. Pada paper ini, requirements recovery bersumber dari end-to-end interaction antara user dan sistem komputer. Dari existing software, diidentifikasi user dan fitur peranti lunak. Selanjutnyadiobservasi end-to-end interaction yang terjadi antara user dan sistem komputer. Hasil identifikasi dan observasi tersebut digunakan untuk membangun ontologi. Ontologi dapat merepresentasikan pengetahuan tentang existing software yang menyiratkan requirements.Kata kunci : Software Reengineering, Reverse Engineering, Requirements Recovery, End-to-endInteraction, Ontologi

The feature extraction for classifying words on social media with the Naïve Bayes algorithm Arif Ridho Lubis; Mahyuddin Khairuddin Matyuso Nasution; Opim Salim Sitompul; Elviawaty Muisa Zamzami
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp1041-1048

To classify Naïve Bayes classification (NBC), however, it is necessary to have a previous pre-processing and feature extraction. Generally, pre-processing eliminates unnecessary words while feature extraction processes these words. This paper focuses on feature extraction in which calculations and searches are used by applying word2vec while in frequency using term frequency-Inverse document frequency (TF-IDF). The process of classifying words on Twitter with 1734 tweets which are defined as a document to weight the calculation of frequency with TF-IDF with words that often come out in tweet, the value of TF-IDF decreases and vice versa. Following the achievement of the weight value of the word in the tweet, the classification is carried out using Naïve Bayes with 1734 test data, yielding an accuracy of 88.8% in the Slack word category tweet and while in the tweet category of verb 78.79%. It can be concluded that the data in the form of words available on twitter can be classified and those that refer to slack words and verbs with a fairly good level of accuracy. so that it manifests from the habit of twitter social media user.

Implementation of ANN with the Cyclical Order Method For Forecasting the Life Span of the World’s Population Muhammad Rizal; Elviawaty Muisa Zamzami
IJISTECH (International Journal of Information System and Technology) Vol 3, No 1 (2019): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v3i1.41

This study aims to predict the age (life expectancy) of the world's population. This research is the development of research that has been done before. But in this study only to get the best architectural model to predict the age (life expectancy) of the world's population, using the Cyclical Order method. Whereas in this follow-up research, it will produce forecasting in the form of age (life expectancy) of the population in the world based on a model that has been obtained from previous research. The research data is the age data (life expectancy) of the world's population from the United Nations: "World Population Prospect: The 2010 Revision Population Database". This study uses 5 architectural models including: 3-5-1, 3-8-1, 3-10-1, 3-5-8-1 and 3-5-10-1. Of the 5 models used, architectural models 3-5-10-1 are the best with an accuracy of 97%, the value of MSE training is 0,0009979400 and MSE testing is 0,0008358919. Forecasting results from this study are expected to be a reference for governments in the world, especially Indonesia to pay more attention to the level of health and well-being of its population so that the level of life of the population is getting better and higher.

Cryptographic Symmetry Analysis with AES Algorithm for Safeguarding Data at Government Agencies Muhammad Rizal; Elviawaty Muisa Zamzami; Muhammad Zarlis
IJISTECH (International Journal of Information System and Technology) Vol 3, No 1 (2019): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v3i1.42

Data is very confidential information because it contains important information about the company or agency. Therefore it is necessary to secure these important data, in order to avoid misuse, theft of information or manipulation by certain parties who are not responsible. To avoid theft and manipulation of data it is necessary to implement a computer-based security system. One of them is by using cryptography. Cryptography is the study of how to change information from a normal (understandable) state / form into an incomprehensible form. One method that can be used to secure messages or information is the Advanced Encryption Standard (AES). AES is a part of symmetry cryptography. The application of AES cryptographic algorithms in securing data at government agencies can produce encryption that cannot be understood by humans and produce decryption that is exactly the same as the initial plaintext inputted, so that important government data can be secured in such a way. This research will produce a program that can encrypt and decrypt data using the AES (Advanced Encryption Standard) algorithm which will be used to secure data at Government Agencies.

Pengaruh Sistem Teknologi Informasi Pada Manajemen Data Dan Informasi Dalam Layanan Keperawatan: Literature Review Indah Mulyani; Elviawaty Muisa Zamzami; Niskarto Zendrato
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol 9, No 2 (2019): Jurnal Inspiration Volume 9 Issue 2
Publisher : STMIK AKBA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v9i2.2526

Tujuan penulisan artikel ini adalah untuk mengidentifikasi pengaruh Sistem Teknologi Informasi pada Manajemen Data dan Proses Informasi dalam Layanan Keperawatan. Metode yang digunakan ialah dengan melakukan pencarian artikel melaui basis data elektronik: ProQuest, EmeraldInsight dan Google Scholar, menggunakan kata kunci Informasi Sistem,. Jumlah artikel yang ditinjau adalah sebanyak 15 artikel yang terkait. Hasil: dari kajian literatur yang dilakukan di dapatkan hasil bahwa Sistem teknologi informasi memberikan dampak yang efektif dan efesien dalam pelayanan keperawatan, perawat dapat meminimalkan waktu untuk melengkapi administrasi pasien, dan kegiatan inti lainnya dari manual menjadi komputerisasi. Sistem Informasi teknologi dalam keperawatan juga bertujuan untuk menjaga keamanan dan kerahasiaan data pasien, memberikan dan menerima informasi yang bermanfaat dan akurat bagi profesi lainnya. Dapat ditarik kesimpulan bahwa Sistem Manajemen Penggunaan informasi dalam ruang lingkup kesehatan dan khususnya pada bidang keperawatan telah memberikan dampak positif bagi pengembangan dan peningkatan sektor kesehatan juga telah memberikan kontribusi dalam meningkatkan kualitas layanan keperawatan bagi masyarakat.

Aplikasi Edutainment Pendukung Pembelajaran Jarak Jauh TK Merujuk Standar Nasional PAUD Elviawaty Muisa Zamzami
Jurnal Obsesi : Jurnal Pendidikan Anak Usia Dini Vol 5, No 2 (2021)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/obsesi.v5i2.750

Pandemi COVID-19 memicu perubahan pembelajaran Nasional, memunculkan Belajar Dari Rumah. Penyelenggaraan Pendidikan menjadi Pembelajaran Jarak Jauh (PJJ), tidak terkecuali untuk jenjang Taman Kanak-kanak (TK). PJJ membutuhkan teknologi informasi dan komunikasi termasuk aplikasi edutainment. Aplikasi edutainment dapat mendukung pembelajaran pada jenjang TK. Paper ini memuat penelitian untuk memperoleh aplikasi-aplikasi edutainment yang dapat mendukung PJJ TK merujuk Standar Nasional PAUD (SN PAUD). Penelitian kualitatif ini menggunakan teknik observasi terhadap aplikasi-aplikasi pada Google Play Store dengan platform Android. Selanjutnya, melakukan analisis eksperimen terhadap konten dari aplikasi edutainment yang telah dikumpulkan. Bagian akhir paper ini memberikan daftar aplikasi edutainment yang penggunaannya mendukung PJJ jenjang TK, bukan untuk menggantikan materi pembelajaran yang telah disusun untuk tatap muka langsung atau daring. Aplikasi-aplikasi edutainment tersebut memuat konten berkesesuaian dengan Standar Isi pada SN PAUD, sehingga siswa memperoleh lebih banyak manfaat Belajar Dari Rumah dan memungkinkan guru mengeksplorasinya untuk pembelajaran.

Peningkatan Akurasi Metode K-Nearest Neighbor dengan Seleksi Fitur Symmetrical Uncertainty Anirma Kandida Br Ginting; Maya Silvi Lydia; Elviawaty Muisa Zamzami
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3254

Accuracy of K-Nearest Neighbor (KNN) tends to be lower than other classification methods. The cause of this is related to the attributes used and the percentage of the influence of these attributes on the classification process in a data. And also attributes with less relevant influence can be a problem in determining the new class. One way that can be done to overcome this is by doing Feature Selection. In this research, the author selects features on K-Nearest Neighbor by using Symmetrical Uncertainty to remove attributes that have an unfavorable effect from the data set. Testing of the proposed method uses data sets obtained from the UCI Machine Learning Repository. The results obtained from testing the proposed method using feature selection with Symmetrical Uncertainty are able to increase the classification accuracy of KNN, with an increase in accuracy obtained after feature selection is 3.00 %.

Cryptographic Symmetry Analysis with AES Algorithm for Safeguarding Data at Government Agencies Muhammad Rizal; Elviawaty Muisa Zamzami; Muhammad Zarlis
IJISTECH (International Journal of Information System and Technology) Vol 3, No 1 (2019): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Data is very confidential information because it contains important information about the company or agency. Therefore it is necessary to secure these important data, in order to avoid misuse, theft of information or manipulation by certain parties who are not responsible. To avoid theft and manipulation of data it is necessary to implement a computer-based security system. One of them is by using cryptography. Cryptography is the study of how to change information from a normal (understandable) state / form into an incomprehensible form. One method that can be used to secure messages or information is the Advanced Encryption Standard (AES). AES is a part of symmetry cryptography. The application of AES cryptographic algorithms in securing data at government agencies can produce encryption that cannot be understood by humans and produce decryption that is exactly the same as the initial plaintext inputted, so that important government data can be secured in such a way. This research will produce a program that can encrypt and decrypt data using the AES (Advanced Encryption Standard) algorithm which will be used to secure data at Government Agencies.

The effect of the TF-IDF algorithm in times series in forecasting word on social media Arif Ridho Lubis; Mahyuddin K. M. Nasution; Opim Salim Sitompul; Elviawaty Muisa Zamzami
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp976-984

Forecasting is one of the main topics in data mining or machine learning in which forecasting, a group of data used, has a label class or target. Thus, many algorithms for solving forecasting problems are categorized as supervised learning with the aim of conducting training. In this case, the things that were supervised were the label or target data playing a role as a 'supervisor' who supervise the training process in achieving a certain level of accuracy or precision. Time series is a method that is generally used to forecast based on time and can forecast words in social media. In this study had conducted the word forecasting on twitter with 1734 tweets which were interpreted as weighted documents using the TF-IDF algorithm with a frequency that often comes out in tweets so the TF-IDF value is getting smaller and vice versa. After getting the word weight value of the tweets, a time series forecast was performed with the test data of 1734 tweets that the results referred to 1203 categories of Slack words and 531 verb tweets as training data resulting in good accuracy. The division of word forecasting was classified into two groups i.e. inactive users and active users. The results obtained were processed with a MAPE calculation process of 50% for inactive users and 0.1980198% for active users.

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