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INDONESIA
Faktor Exacta
Published by Universitas Indraprasta PGRI
ISSN : 1979276X     EISSN : 2502339X     DOI : -
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering
Faktor Exacta is a peer review journal in the field of informatics. This journal was published in March (March, June, September, December) by Institute for Research and Community Service, University of Indraprasta PGRI, Indonesia. All newspapers will be read blind. Accepted papers will be available online (free access) and print version.
Arjuna Subject : -
Articles 8 Documents
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Search results for , issue "Vol 16, No 3 (2023)" : 8 Documents clear
Penerapan Algoritma C4.5 dengan Optimasi Particle Swarm Optimization untuk Prediksi Kelulusan Mahasiswa Hermawati, Mercy
Faktor Exacta Vol 16, No 3 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i3.17296

Abstract

College is a place for students to pursue higher education. Both state and private universities compete to be the best universities to produce the best graduates. The number of student graduates is an indicator of the success of a higher education institution, which will have an impact on government accreditation and public assessment. The aim of this research is to predict student graduation in order to know whether they will graduate on time or late by applying data mining techniques, namely classification using the C4.5 algorithm to obtain patterns of student graduation delays and the particle swarm optimization (PSO) algorithm to increase the accuracy of the C4 algorithm. 5. Testing uses cross validation tests, confusion matrix and ROC curve. The results of this research are that the C4.5 algorithm with particle swarm optimization (PSO) has an accuracy value of 86.72%, which is better than the C4.5 algorithm, whose accuracy is 82.05% and the difference between them is 4.67%. The difference between the AUC value of 0.033 was obtained from the C4.5 algorithm model, which had an AUC value of 0.870 with a good classification diagnostic level, and the C4.5 algorithm with PSO had an AUC value of 0.903 with an excellent classification diagnostic level. IPS3 is the attribute that most influences the accuracy of student graduation. The results of the C4.5 algorithm rule with PSO can be applied to create applications for GUI-based student graduation predictions.
Kerangka Kerja Evaluasi dalam Menentukan Kendaraan Kargo yang Optimal Menggunakan Analytic Hierarchy Process Setiawan, Santoso; Sulistyowati, Daning Nur
Faktor Exacta Vol 16, No 3 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i3.19415

Abstract

This research aims to develop an evaluation framework in determining the optimal cargo vehicle (freight transportation) using the Analytic Hierarchy Process (AHP) method. The selection of the right cargo vehicle is crucial in logistics management to ensure the efficiency and sustainability of business operations. This research, combines the AHP approach with multi-criteria evaluation to help make better decisions. The proposed evaluation framework consists of several steps, namely: determining relevant criteria in cargo vehicle selection, including factors such as reliability, fuel efficiency, and vehicle price. Then collecting data related to these criteria from reliable sources. Next, conduct a pairwise comparison analysis with AHP to obtain the relative weight of each criterion. And finally calculate the relative performance value for each cargo vehicle based on the set criteria. In this study, the authors applied the proposed evaluation framework to a logistics operation case study. The results show that the use of the AHP method in cargo vehicle selection can help make more systematic and objective decisions. This evaluation framework also allows stakeholders to identify the cargo vehicle that best suits their needs.
Terapan Metode Least Significant Bit untuk Deteksi Keaslian e-Sertifikat Primawati, Alusyanti; Paramita, Aulia; Muchbarak, Akbar; Sulistyohati, Aprilia
Faktor Exacta Vol 16, No 3 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i3.17314

Abstract

The ease of accessing digital information allows a person to change or manipulate the data contained in the information. Therefore, the security of information or vital data from intruders or unauthorized access is important. One of the cases that are currently happening is the distribution and falsification of information through fake e-certificates. The purpose of this study is to improve security, and check the authenticity and validity of data on e-certificates using steganography techniques with the Least Significant Bit (LSB) method. Steganography is used to disguise confidential information in digital media so that confidential information is difficult to detect by unauthorized parties. The image on the certificate in the form of a pdf file will be inserted with information to validate whether the certificate is genuine or not, by reading the pixels in the image in the file. In this study, the data to be inserted is in the form of a checksum value in the form of 32 hexadecimal characters and also e-certificate information in the form of a JSON string. The results of this study are a website-based application that is capable of checking the authenticity of e-certificates.
ANALISIS KEBERHASILAN STUDI AWAL MAHASISWA MENGGUNAKAN KLASTERISASI K-MEANS Painem, Painem; Soetanto, Hari; Solichin, Achmad
Faktor Exacta Vol 16, No 3 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i3.19539

Abstract

Mahasiswa merupakan salah satu elemen penting dalam perkuliahan di perguruan tinggi. Setiap mahasiswa yang menempuh kuliah di suatu perguruan tinggi tentunya menginginkan dapat lulus tepat waktu dengan memenuhi kualifikasi akademik yang optimal. Demikian juga bagi pihak program studi dan universitas, keberhasilan studi mahasiswa merupakan salah satu indikator penting dalam keberhasilan penyelenggaraan pendidikan di perguruan tinggi. Analisis keberhasilan studi mahasiswa seharusnya dilakukan secara berkala mulai dari awal studi hingga akhir studi. Hasil analisis keberhasilan studi dapat dijadikan dasar dalam pengambilan keputusan dan evaluasi program pembelajaran bagi program studi maupun universitas. Namun demikian, melakukan analisis keberhasilan studi mahasiswa pada sebuah perguruan tinggi dengan jumlah mahasiswa yang cukup banyak terkadang sulit dilakukan dan cukup rumit Pengelola universitas dan/atau program studi seringkali kesulitan dalam menyusun program pembelajaran yang tepat sasaran bagi mahasiswa dalam rangka menghasilkan lulusan yang memiliki kemampuan akademik yang optimal dan lulus tepat waktu. Untuk membantu ketua program studi dalam melakukan analisis keberhasilan studi awal mahasiswa adalah dengan metode klusterisasi k-means. Berdasarkan analisa keberhasilan studi awal mahasiswa menggunakan kalsterisasi K- means maka mahasiswa yang masuk ke klaster 0 adalah  22,6 % atau sebanyak 3055 mahasiswa, sedangkan yang masuk ke klaster 1 adalah 69,5 % atau sebanyak 9405 mahasiswa dan yang masuk ke dalam klaster 2 adalah 7,9 % atau 1066 mahasiswa
Application of 2DPCA and SOM Algorithms to Identification of Digital Signature Ownership Norhikmah, Norhikmah
Faktor Exacta Vol 16, No 3 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i3.17504

Abstract

Signature Is one of the proofs for ratification, one of which is a thesis document, with the development of the  when conventional signatures have begin to switch to digital signatures, where digital signatures already have a legal umbrella in Indonesia, currently Covid is still hitting Indonesia, forcing some agencies change the ratification of a document using a digital signature, so that it can provide an opening for falsifying digital signature ratification. Therefore, an application is needed to identify the ownership of a digital signature image, with the first research stage is to collect a digital signature dataset in the form of a signature image or take a dataset from a published legal document, an example of a second stage publication manages the image processing with grayscale first to get extra features and then analyzes the extra feature image using 2DPCA, and identification To get the best matching of image units using the Single Organizing Maps (SOM) method. the results of this study are using the 2DPCA algorithm and SOM to identify ownership of digital signatures, with 84 correct and incorrect test results, from a total dataset of 91 patterns. And get the highest accuracy value of 92.3% at a 20000 translation and a rate of 0.9.
Sistem Pakar Rekomendasi Pendakian Gunung di Jawa Tengah menggunakan Algoritma Fuzzy Tsukamoto Berbasis Website Anggun Pratiwi, Cut Tesya Iftillah; Norhikmah, Norhikmah
Faktor Exacta Vol 16, No 3 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i3.17685

Abstract

Indonesia has a variety of unique tours both in terms of nature and history that can be used as tourist attractions for local and foreign people, with a variety of beauties that can be enjoyed. One of them is the mountainous area which presents views that can soothe and spoil the eyes in almost every area of Indonesia which has different characteristics of mountains, especially the island of Java, to be precise in the area of Central Java, has about 12 mountains which are usually used as climbing places for residents in or outside the region, in addition to helping the people's economy, climbing can also be used to protect nature and can introduce the beauty of nature in Indonesia to the international community. Therefore a system was created to recommend mountain climbing in the Central Java area based on a website using the Fuzzy Tsukamato method
Analisis Sentimen Pindah Ibu Kota Negara (IKN) Baru pada Twitter Menggunakan Algoritma Naive Bayes dan Support Vector Machine (SVM) Siregar, Amril Mutoi
Faktor Exacta Vol 16, No 3 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i3.16703

Abstract

Pemindahan Ibu Kota Negara (IKN) Indonesia merupakan salah satu topik yang sedang menjadi sorotan bahkan trending topik di Twitter, sehingga menimbulkan pro kontra bagi masyarakat. Topik tersebut sudah menjadi sumber perdebatan bagi pengguna Twitter. Untuk mengetahui para pengguna twitter dalam mengemukakan pendapatnya dapat dilakukan dengan cara analisis sentimen, dimana cara tersebut memisahkan opini berdasarkan positif dan negatif. Pada analisis sentimen, metode yang digunakan biasanya menggunakan Naïve Bayes dan Support Vector Machine (SVM). Dengan dilakukannya analisa sentimen pada pemindahan IKN Indonesia dengan menggunakan dua metode algoritma yaitu Naïve Bayes dan SVM, maka permasalahan yang menjadi kontroversi dapat diketahui, sehingga dapat menjadi bahan evaluasi untuk kepentingan lainnya. Selain itu juga dengan penggunaan dua metode algoritma tersebut diharapkan dapat diketahui metode algoritma mana yang dapat menunjukkan tingkat akurasi yang tepat. Berlandaskan uraian tersebut, maka penelitian kali ini perlu memberikan kontribusi baru dalam mengalisis sentimen IKN Indonesia dengan menggunakan dua metode yang berbeda, sehingga penelitian berbeda dari penelitian-penelitian terdahulu. Penelitian ini bertujuan untuk menganalisis dan mengetahui sentimen masyarakat Indonesia terhadap pemindahan IKN melalui cuitan pada aplikasi Twitter. Untuk melakukan analisis sentimen tersebut, peneliti menggunakan dataset dari Twitter guna mengetahui perbandingan keakurasian diantara dua metode yang digunakan yaitu Naïve Bayes untuk mengkategorikan cuitan kedalam 2 kategori yaitu cuitan positif dan negatif, kemudian dibandingkan dengan metode SVM. Penelitian dilaksanakan sebagai pendukung informasi yang akurat kepada masyarakat terhadap Ibu Kota Negara. Metode penelitian yang digunakan yaitu klasifikasi Naïve Bayes dan klasifikasi SVM dengan dukungan tools Rapidminer. Hasil analisis sentimen dengan algoritma Naïve Bayes menghasilkan akurasi 86.94% memiliki nilai presisi rata-rata 96.24%, dan nilai recall 86.66%. Sedangkan hasil analisis dengan algoritma SVM menghasilkan nilai akurasi sejumlah 90.81%. Hasil analisis sentimen penelitian ini memiliki nilai presisi rata-rata sebesar 90.12%, dan nilai recall sebesar 99.12%.
Sistem Pakar Diagnosa Penyakit Berisiko Di Setiap Status Gizi Berdasarkan Indeks Massa Tubuh Menggunakan Metode Hy-brid Case Base Pratama, Rifqhy Rayhan Andi Riga; Norhikmah, Norhikmah
Faktor Exacta Vol 16, No 3 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i3.17686

Abstract

Nutritional status is an indicator of health for a person which is divided into four classifications, namely, Obesity, Overweight, Normal Weight, and Underweight. and nutritional status can be obtained by calculating the body mass index (BMI) of the person by dividing the body weight (kg) by the height (m2). after knowing the nutritional status, a diagnosis can be made by applying the Hybrid Case Base method. Applicationing of this method is too precise because it can provide accurate diagnostic results because this method combines two methods, namely Case Based Reasoning and Rule Based Reasoning. With this expert system, it can help people to find out their nutritional status and whether they have a risky disease or not, especially for people who don't have a lot of free time to consult with doctors so they can do it only by using the expert system that has been created. This method is quite fast and practical and provides accurate diagnostic results as well.

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