Garuda - Garba Rujukan Digital

PELATIHAN PENGGUNAAN PLATFORM DIGITAL SEBAGAI PENERAPAN ECCOMERCE PADA KEGIATAN UMKM Faradillah Faradillah; Lastri Widya Astuti; Leriza Desitama Anggraini; Muhammad Fadhiel Alie
Jurnal Abdimas Mandiri Vol 6, No 1
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jam.v6i1.2084

Kegiatan pengabdian kepada masyarakat ini dilakukan melalui pelatihan yang diberikan kepada pelaku UMKM yaitu Pelaku Usaha Clothing Line dalam penggunaan platform digital sebagai penerapan e-Commerce yang dapat menunjang kegiatan promosi, pemasaran dan penjualan pada era digital 4.0. Kegiatan ini dilakukan melalui beberapa tahapan yaitu: persiapan, pelaksanaan, penutup dan penyusunan laporan akhir kegiatan. Pelatihan dilakukan dalam dua bentuk metode yaitu penyampaian materi dan bimbingan teknis penggunaan platform digital yaitu website e-Commerce (dummy marketplace) terkait digital content pada website tersebut. Berdasarkan seluruh tahapan yang dilakukan diperoleh bahwa melalui kegitan ini menghasilkan peningkatan wawasan, pemahaman dan keterampilan peserta terkait e-Commerce dan penggunaan platform digital, adanya sharing session yang memberikan wadah para peserta berbagi pengalaman dan kendala terkait kegiatan penjualan dan penggunaan teknologi selama ini, kegiatan ini juga memberi manfaat berupa perluasan jaringan dan ide pengembangan usaha sesama pelaku usaha clothing line. Berdasarkan hasil penyebaran kuesioner yang dilakukan pada akhir kegiatan diperoleh bahwa kegiatan pengabdian kepada masyarakat ini memberi manfaat, adanya perubahan mindset terkait penggunaan platform digital oleh peserta, adanya peningkatan wawasan terkait strategi promosi, pemasaran dan penjualan melalui content digital dirasakan oleh peserta kegiatan. Diharapkan kegiatan ini dilaksanakan secara berkala sehingga dapat membantu pelaku usaha Clothing Line meningkatkan keterampilan dalam menerapkan e-Commerce sehingga dapat meingkatkan keuntungan pada usahanya.Kata kunci : e-Commerce, Platform, Digital

RANCANG BANGUN DAN SOSIALISASI SISTEM PENGELOLAAN ARSIP SURAT BERBASIS WEB PADA SMA NEGERI 12 PALEMBANG Lastri Widya Astuti; Siti Muyaroah; Leriza Desithama; M Fadhiel Alie
Jurnal Abdimas Mandiri Vol 4, No 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jam.v4i2.1273

Pengarsipan surat di Sekolah Menengah Atas (SMA) Negeri 12 Palembang masih menggunakan cara konvesional dalam mengelola sistem pengarsipan surat. Kendala yang sering dijumpai adalah sulitnya mencari file surat, baik surat masuk maupun surat keluar. Proses pencarian surat pada arsip memakan cukup banyak waktu terlebih lagi nama file surat tidak selalu bisa di ingat. Penggunaan aplikasi bantu diharapkan dapat membantu dan memecahkan masalah yang dihadapi oleh pihak sekolah. Rancang bangun sistem pengelolaan surat berbentuk aplikasi pengarsipan surat berbasis website di Sekolah Menengah Atas (SMA) Negeri 12 Palembang diharapkan dapat membantu pekerjaan staf tata usaha dalam mengelolah surat-surat agar menjadi lebih terpelihara dengan teratur, baik, aman dan efisien serta surat tersebut dapat ditemukan dengan cepat dan juga akurat.Kata kunci : arsip, surat, website, aplikasi, pencarian

PELATIHAN DAN PENDAMPINGAN AUDIT INTERNAL SISTEM INFORMASI MANAJEMEN DI DINAS PENANAMAN MODAL DAN PELAYANAN TERPADU SATU PINTU KABUPATEN OGAN KOMERING ILIR Faradillah Faradillah; Lastri Widya Astuti; Endah Dewi Purnamasari; Leriza Desitama
Jurnal Abdimas Mandiri Vol 5, No 1
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jam.v5i1.1500

Pendampingan audit internal ini bertujuan untuk memberikan pemahaman tentang pengendalian internal terhadap risiko-risiko umum teknologi dalam hal ini Sistem Informasi Manajemen yang digunakan di DPMPTSP. Kegiatan ini juga bertujuan membantu instansi terkait dalam meningkatkan pengendalian internal terhadap penggunaan teknologi sebagai bentuk peningkatan mutu internal. Audit sistem informasi adalah proses pengumpulan dan penilaian bukti – bukti untuk menentukan apakah sistem komputer dapat mengamankan aset, memelihara integritas data, dapat mendorong pencapaian tujuan organisasi secara efektif dan menggunakan sumberdaya secara efisien. Auditing adalah sebuah proses sistematis untuk secara obyektif mendapatkan dan mengevaluasi bukti mengenai pernyataan perihal tindakan dan transaksi bernilai ekonomi, untuk memastikan tingkat kesesuaian antara pernyataan tersebut dengan kriteria yang telah ditetapkan, serta mengkomunikasikan hasilnya pada para pemakai yang berkepentingan. Pengendalian internal harus diterapkan terhadap setiap sistem dan aplikasi, hal ini dilakukan untuk mengurangi exposure yang selalu muncul pada pencatatan yang buruk, akuntansi yang tidak tepat, interupsi bisnis, pengambilan keputusan yang buruk, penipuan dan penggelapan, pelanggaran hukum terhadap peraturan, peningkatan biaya dan hilangnya aset perusahaan. Dalam kegiatan ini dilakukan evaluasi awal tentang kondisi saat ini untuk mengetahui efektivitas pelaksanaan pengendalian internal terhadap teknologi. Berdasarkan hasil evaluasi yang dilakukan diperoleh bahwa terdapat dokumen pengendalian internal berupa SOP yang telah diterapkan dengan baik, namun beberapa hal perlu ditambahkan untuk meningkatkan keamanan sistem.Kata kunci : SIM, ISO, Pengendalian Internal

Penggunaan Metode Signature Based dalam Pengenalan Pola Serangan di Jaringan Komputer Herri Setiawan; M. Agus Munandar; Lastri Widya Astuti
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8, No 3: Juni 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021834200

Masalah keamanan jaringan semakin menjadi perhatian saat ini. Sudah semakin banyak tools maupun teknik yang dapat digunakan untuk masuk kedalam sistem secara ilegal, sehingga membuat lumpuh sistem yang ada. Hal tersebut dapat terjadi karena adanya celah dan tidak adanya sistem keamanan yang melindunginya, sehingga sistem menjadi rentan terhadap serangan. Pengenalan pola serangan di jaringan merupakan salah satu upaya agar serangan tersebut dapat dikenali, sehingga mempermudah administrator jaringan dalam menanganinya apabila terjadi serangan. Salah satu teknik yang dapat digunakan dalam keamanan jaringan karena dapat mendeteksi serangan secara real time adalah Intrusion Detection System (IDS), yang dapat membantu administrator dalam mendeteksi serangan yang datang. Penelitian ini menggunakan metode signatured based dan mengujinya dengan menggunakan simulasi. Paket data yang masuk akan dinilai apakah berbahaya atau tidak, selanjutnya digunakan beberapa rule untuk mencari nilai akurasi terbaik. Beberapa rule yang digunakan berdasarkan hasil training dan uji menghasilakan 60% hasil training dan 50% untuk hasil uji rule 1, 50% hasil training dan 75% hasil uji rule 2, 75% hasil training dan hasil uji rule 3, 25% hasil training dan hasil uji rule 4, 50% hasil training dan hasil uji untuk rule 5. Hasil pengujian dengan metode signatured based ini mampu mengenali pola data serangan melaui protokol TCP dan UDP, dan monitoring yang dibuat mampu mendeteksi semua serangan dengan tampilan web base.AbstractNetwork security issues are becoming increasingly a concern these days. There are more and more tools and techniques that can be used to enter the system illegally, thus paralyzing the existing system. This can occur due to loopholes and the absence of a security system that protects it so that the system becomes vulnerable to attacks. The recognition of attack patterns on the network is an effort to make these attacks recognizable, making it easier for network administrators to handle them in the event of an attack. One of the techniques that can be used in network security because of a timely attack is the Intrusion Detection System (IDS), which can help administrators in surveillance that comes. This study used a signature-based method and tested it using a simulation. The incoming data packet will be assessed whether it is dangerous or not, then several rules are used to find the best accuracy value. Some rules used are based on the results of training and testing results in 60% training results and 50% for rule 1 test results, 50% training results and 75% rule 2 test results, 75% training results and rule 3 test results, 25% training results and the result of rule 4 test, 50% of training results and test results for rule 5. The test results with the signature-based method can recognize attack data patterns via TCP and UDP protocols, and monitoring is made to be able to detect all attacks with a web-based display.

PENGENALAN POLA SUARA MANUSIA BEREKSTENSI FILE WAV MENGGUNAKAN METODE FAST FOURIER TRANSFORM DAN BAYES Nurlia Apriani; Rita Wiryasaputra; Lastri Widya Astuti
Computatio : Journal of Computer Science and Information Systems Vol 2, No 1 (2018): Computatio : Journal of Computer Science and Information Systems
Publisher : Faculty of Information Technology, Universitas Tarumanagara

The human voice is a very unique sound wave. That's because every human being has a different kind of sound wave. The fundamental difference in human voice is high the low the sound level associated with the signal from sound waves. The purpose of this research is to know the accuracy result from Fast Fourier Transform and Bayes method in pattern recognition. The Fast Fourier Transform method is used for feature extraction and Bayes method is used to calculate the sound probability value between the train data and test data, then Bayes Method is used to determine the result of the introduction of some previously stored train data. This research was made using Matlab R2016a, by matching the pattern of human sound that has been made before or called train data with new sound pattern or called test data. Testing is done on voice in the database and the voice is not in the database. Test results for voice in the database were 96% for first men and 76% for first women. While testing for voice is not in the database is 46% for second men and 50% for second women

Optimalisasi Klasifikasi Kanker Payudara Menggunakan Forward Selection pada Naive Bayes Lastri Widya Astuti; Imelda Saluza; Faradilla Faradilla; M. Fadhiel Alie
Jurnal Informatika Global Vol 11, No 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v11i2.1235

Breast cancer is a type of malignant tumor which is still the number one killer where the process of spread or metastasis takes a long time. The number of breast cancer sufferers increases every year so that if detected or caught early, prevention can be done early so as to reduce the number of breast cancer sufferers. To reduce the risk of increasing the number of cancer patients, it is necessary to do early detection, several methods can be used to assist the early detection process such as cancer screening, or computational methods. Several machine learning methods that have been chosen to solve cases of breast cancer prediction, especially the classification algorithm, including Naive Bayes have the advantage of being simple but having high accuracy even though they use little data. Weaknesses in Naive Bayes, namely the prediction of the probability result is not running optimally and the lack of selection of relevant features to the classification so that the accuracy is low. This research is intended to build a classification system for detecting breast cancer using the Naive Bayes method, by adding a forward selection method for feature selection from the many features that exist in breast cancer data, because not all features are features that can be used in the classification process. The result of combining the Naive Bayes method and the forward selection method in feature selection can increase the accuracy value of 96.49% detection of breast cancer patients.

Ekstrasi Fitur Citra MRI Otak Menggunakan Data Wavelet Transform (DWT) untuk Klasifikasi Penyakit Tumor Otak Lastri Widya Astuti
Jurnal Informatika Global Vol 10, No 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

ABSTRACTThe brain is formed from two types of cells: glia and neurons. Glia functions to support and protect neurons, while neurons carry information in the form of electrical pulses known as potential action. The brain regulates and coordinates most of the body's movements, behavior, and homeostasis functions such as heart rate, blood pressure, body fluid balance and body temperature. A brain tumor is a mass of abnormally growing brain cells. Most brain tumors can spread through brain tissue, but rarely spread to other areas of the body. But in the case of benign brain tumors, as they grow they can destroy and suppress other normal brain tissue, which can result in paralysis. Several methods are used to detect disorders of the brain nerve tissue, including: Magnetic Resonance Imaging (MRI). This research is intended to build a classification system for brain image data using Magnetic Resonance Imaging (MRI) with the category, normal, Glioma, metastatic bronchogenic carcinoma or Alzheimer's using Magnetic Resonance Imaging (MRI) so that it can assist in decision making for medical experts. While the method used in this research is Discrete Wavelet Transformation (DWT) for the feature extraction process so that the unique characteristics of an object can be recognized, as well as the classification process using the adaptive neighborhood neural network method. This research is able to integrate the two methods with the acquisition of significant accuracy.Keywords : feature extraction, classification, MRI, BrainABSTRAKOtak terbentuk dari dua jenis sel: glia dan neuron. Glia berfungsi untuk menunjang dan melindungi neuron, sedangkan neuron membawa informasi dalam bentuk pulsa listrik yang di kenal sebagai potensi aksi. Otak mengatur dan mengkordinir sebagian besar,gerakan, perilaku dan fungsi tubuh homeostasis seperti detak jantung, tekanan darah, keseimbangan cairan tubuh dan suhu tubuh. Tumor otak adalah sekumpulan massa sel-sel otak yang tumbuh abnormal. Sebagian besar tumor otak dapat menyebar melalui jaringan otak, tetapi jarang sekali menyebar ke area lain dari tubuh. Namun pada kasus tumor otak yang jinak, saat mereka tumbuh dapat menghancurkan dan menekan jaringan otak normal lainnya, yang dapat berakibat pada kelumpuhan. Beberapa metode dipergunakan untuk mendeteksi gangguan pada jaringan syaraf otak, diantaranya: Magnetic Resonance Imaging (MRI). Penelitian ini dimaksudkan untuk membangun sistem klasifikasi untuk data citra otak menggunakan Magnetic Resonance Imaging (MRI) dengan kategori, normal, Glioma, metastatic bronchogenic carcinoma atau Alzheimer menggunakan Magnetic Resonance Imaging (MRI) sehingga dapat membantu dalam pengambilan keputusan bagi tenaga ahli dibidang kedokteran. Sedangkan metode yang digunakan dalam penelitian adalah Discrete Wavelet Transformation (DWT) untuk proses ekstrasi fitur (feature extraction) agar karakteristik unik dari suatu objek dapat dikenali, serta proses klasifikasi menggunakan metode adaptive neighborhood neural network. Penelitian ini mampu mengintegrasikan kedua metoda dengan perolehan hasil akurasi yang signifikan.Kata kunci : ekstrasi fitur, klasifikasi, MRI, Otak

Feature Selection Menggunakan Binary Wheal Optimizaton Algorithm (BWOA) pada Klasifikasi Penyakit Diabetes Lastri Widya Astuti; Imelda Saluza; Evi Yulianti; Dhamayanti Dhamayanti
Jurnal Informatika Global Vol 13, No 1
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v13i1.2057

Diabetes Mellitus (DM) is a chronic disease characterized by blood glucose (blood sugar) levels exceeding normal, i.e. blood sugar levels being equal to or more than 200 mg/dl, and fasting blood sugar levels being above or equal to 126 mg/dl. The increase in the number of people with diabetes is due to delays in detection. Utilization of machine learning in helping to establish a fast and accurate diagnosis is one of the efforts made in the health sector. One of the important steps to produce high classification accuracy is through the selection of relevant features. The problem in feature selection is dimensionality reduction, where initially all attributes are required to obtain maximum accuracy while not all features are used in the classification process. This study uses the Binary wheal Optimization Algorithm (BWOA) as a feature selection method to increase accuracy in the classification of diabetes mellitus. The use of metaheuristic algorithms is an alternative to increase computational efficiency and avoid local minimums. The BWOA algorithm reduces the 8 attributes in the dataset to the 3 best attributes that are able to represent the original dataset. The results showed that from the six classification methods tested, namely: K-NN, Naïve Bayes, Random Forest, Logistics Regression, Decision Tree, Neural Network. then the three logistic regression methods, naive Bayes and neural network are in good classification criteria based on Area Under Curve (AUC) while the calculation of the accuracy value shows an average of above 70%. Keywords : Feature Selection, Classification, Diabetes Mellitus, Accuracy, Area Under Curve (AUC)

Prediksi Data Time Series Harga Penutupan Saham Menggunakan Model Box Jenkins ARIMA Imelda Saluza; Dewi Sartika; Lastri Widya Astuti; Faradillah Faradillah; Leriza Desitama; Endah Dewi Purnamasari
Jurnal Informatika Global Vol 12, No 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v12i2.1940

The ability to predict time series data on closing market prices is critical in determining a company's stock results. The development of an efficient stock market has a positive correlation with economic growth, in a country both in the short and long term. In practice, investors tend to invest in countries that have a stable economy, low crime. The rise and fall of stock prices has made many investors develop various effective strategies in predicting stock prices in the future with the aim of making investment decisions so that investors can guarantee their profits and minimize risk.As a result, the researchers developed a model that could accurately estimate precision. Time series data models are one of the most powerful methods to render assumptions in decisions containing uncertainty. The AutoRegressive Integrated Moving Average (ARIMA) model with the Box Jenskins time series procedure is one of the most commonly used prediction models for time series results. The steps for using the Box Jenskins ARIMA model for historical details of expected stock closing prices are outlined in this paper. BBYB and YELO stock data from yahoo.finance were used as historical data. The Aikake Information Criterion (AIC), Bayesian Information Criterion (BIC) / Schawrz Bayesia Criterion (SBC), Log Probability, and Root Mean Square Error (RMSE) are used to choose an effective model, and the model chosen is ARIMA (1 , 1,2). The findings suggest that the Jenkins ARIMA box model has a lot of scope for short-term forecasting, which may help investors make better decisions. Keywords: prediction, the stock's current closing price, Box Jenskins ARIMA model

APLIKASI ISC (INFORMATICS STUDENT CENTER) MENGGUNAKAN METODE PERSONAL EXTREME PROGRAMMING BERBASIS ANDROID Rizka Anjuliani; Lastri Widya Astuti; Hartini Hartini
Jurnal Informatika Global Vol 6, No 1
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Gathering for informations today can be classified as a primary need. Various kinds of activities can be done using technology, especially smartphones, for example, learning, interacting with other people, searching for information, or just for entertainment. There are many kinds of activities and many variety to access them. So it cannot be done all at once in a practical way, we need a solution to be able to carry out these activities at once with easy access. The above issue is one of the problems which is faced by the University of Indo Global Mandiri (UIGM). In order for learning and getting information can be done at once and easily accessible, then the researcher do research that aims to build applications Informatics Student Center (ISC) which is developed by using the Personal Extreme Programming (PXP) consisting of several phases, such as requirements, planning, iteration initialization, design, implementation, and system testing. ISC applications using the Android operating system, built with the collaboration of the Java language, PHP, and MySQL database. In addition to ease of access, the ISC has some features that academic information, discussion forums, entertainment, and job opportunity. Based on test results, it was concluded that the ISC application can run on Android mobile devices which are tested. Result from this study is the availability of applications supporting learning activities and access information that can be accessed online through the Android mobile devices

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