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INDONESIA
Jurnal Informatika
Published by Universitas Ahmad Dahlan
ISSN : 19780524     EISSN : 25286374     DOI : 10.26555
Core Subject : Science,
Computer Science & IT
Arjuna Subject : -
Articles 419 Documents
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PERANCANGAN APLIKASI FRONTDESK SERVER ASSISTANT (FOSA) Dahlan Abdullah; Heri Juni Afisman
Jurnal Informatika Vol 8, No 1 (2014): Januari
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v8i1.a2083

Abstract

Perkembangan teknologi informasi yang sangat pesat turut memajukan media komunikasi sebagai media penyampaian informasi dari suatu tempat ke tempat lainnya, sehingga memudahkan orang dalam mengakses media komunikasi.Dalam faktor keamanan ini biasanya perusahaan menempatkan administrator untuk menjaga tetapi fungsi administrator tentunya akan terbatas waktunya, saat jam kerja.perancangan aplikasi yang melayani para administrator server dalam hal menjaga dan memelihara kestabilan server  tanpa membutuhkan keahlian tertentu dalam  memanfaatkan aplikasi tersebut oleh penulis system tersebut diberi nama Frontdesk Server Assistant (Fosa). Kata kunci : Server, Client, Administrator, Frontdeks Server Assistan (Fosa)
PEMBUATAN APLIKASI SISTEM COMPETENCY BASED DEVELOPMENT PURPOSE BERBASIS WEB Eko Aribowo; Ali Tarmuji; Prasetyo Herlambang
Jurnal Informatika Vol 5, No 1: January 2011
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v5i1.a2792

Abstract

Strategi Perusahaan dalam mencapai visi dan Misi ditetapkan enamlangkah dan salah satunya adalah Penerapan CBHRM (Competency BasedHuman Resources Management). CBHRM adalah strategi perhutani dalammeningkatkan kualitas pegawai Perhutani. Dalam menjalankan strategi ini salahsatu tahapan yang harus dilakukan adalah melakukan pengembangankompentensi atau dinamakan Competency Based Development Purpose (CBDP).Dalam tahap ini Pegawai Perum Perhutani akan dinilai level kompetensi yangmereka miliki untuk mengetahui program pengembangan yang sesuai dengankompetensi yang disyaratkan. Penentuan pelatihan yang dilakukan secara manualmembuat Perum Perhutani membutuhkan waktu yang lama dan pelatihan yangdiikuti karyawan kurang sesuai, selain itu karyawan juga merasa kesulitan untukmemilihnya. Tujuan dari pembuatan aplikasi ini adalah dapat mempermudahPerum Perhutani dalam menentukan pelatihan untuk karyawan yang sesuaidengan hasil tes kompetensi dan memberikan kesempatan kepada karyawan untukmemilih pelatihan yang akan diikutinya. Pengumpulan data dilakukan denganberbagai metode antara lain : studi pustaka, interview dan observasi. Modelproses yang digunakan adalah waterfall yang terdiri dari analisa kebutuhansistem, perancangan, implementasi dan pengujian. Pada perancangan sistemdimulai dari perancangan kebutuhan sistem,perancangan proses, perancangandatabase dan perancangan interface. Pada tahap implementasi menggunakanPHPMyAdmin untuk membangun basisdata dan PHP untuk teknologi ServersideTahap terakhir adalah pengujian sistem yang dilakukan dengan Black Box Testdan Alpha Test. Hasil penelitian ini berupa aplikasi sistem Competency BasedDevelopment Purpose berbasis web yang dapat membantu Perum Perhtani dalammenentukan pelatih untuk pengembangan karyawan yang sesuai dengan hasil teskompetensi yang telah dilakukan sebelumnya oleh karyawan. 
Comparison of machine learning for sentiment analysis in detecting anxiety based on social media data Shoffan Saifullah; Yuli Fauziyah; Agus Sasmito Aribowo
Jurnal Informatika Vol 15, No 1 (2021): January 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i1.a20111

Abstract

All groups of people felt the impact of the COVID-19 pandemic. This situation triggers anxiety, which is bad for everyone. The government's role is very influential in solving these problems with its work program. It also has many pros and cons that cause public anxiety. For that, it is necessary to detect anxiety to improve government programs that can increase public expectations. This study applies machine learning to detecting anxiety based on social media comments regarding government programs to deal with this pandemic. This concept will adopt a sentiment analysis in detecting anxiety based on positive and negative comments from netizens. The machine learning methods implemented include K-NN, Bernoulli, Decision Tree Classifier, Support Vector Classifier, Random Forest, and XG-boost. The data sample used is the result of crawling YouTube comments. The data used amounted to 4862 comments consisting of negative and positive data with 3211 and 1651. Negative data identify anxiety, while positive data identifies hope (not anxious). Machine learning is processed based on feature extraction of count-vectorization and TF-IDF. The results showed that the sentiment data amounted to 3889 and 973 in testing, and training with the greatest accuracy was the random forest with feature extraction of vectorization count and TF-IDF of 84.99% and 82.63%, respectively. The best precision test is K-NN, while the best recall is XG-Boost. Thus, Random Forest is the best accurate to detect someone's anxiety based-on data from social media.
Image denoising using wavelet thresholding and median filter based Raspberry pi Rusul Sabah; Ruzelita Ngadiran; Dalal Abdulmohsin Hammood
Jurnal Informatika Vol 15, No 2 (2021): May 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i2.a20609

Abstract

The goal of any denoising technique is to remove noise from an image which is the first step in any image processing. The noise removal method should be applied watchful manner. Otherwise, artifacts can be introduced, which may blur the image. In this work, three levels of Gaussian noise are used for adding noise on the original image (σ=10, σ=50, σ =100) and also (σ=15, σ=20, σ=25) to compare with existing work and analysis with it to test embedded system with a median filter. Performance evaluation of the median filter, wavelet threshold denoising techniques is provided. The techniques used are the median filter and wavelet threshold used to remove noise based on raspberry pi with Python. Four methods to remove noise images are used. (Median Filter, Wavelet Thresholding) MF, WT, MF before and after WT. The results showed the camera image was better than the other after tested all the methods with Gaussian noise σ=10. On the other hand, the other images were better than the camera images for the Gaussian levels 50 and 100. The results were good in the median filter in wavelet threshold based on Raspberry Pi, which is compared with most of the images butter in the median filter.
Content-based recommender system architecture for similar e-commerce products Ari Nurcahya; Supriyanto Supriyanto
Jurnal Informatika Vol 14, No 3 (2020): September 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i3.a18511

Abstract

Recommendation systems are quite famous and are increasingly being used on e-commerce platforms for a variety of purposes. The recommendation system technique used also varies greatly depending on the scope and Item of recommendation. Content-based filtering, for example, is used to recommend related product items based on user preferences. However, how the recommendation system architecture should be built starts by creating a data model for bringing up related product items. This paper offers a system architecture by considering the initial problem usually faced by recommendation systems, namely the cold start problem. The problem of lack of user preferences data is trying to be overcome by utilizing product item documents. Product item documents are processed using the TF-IDF algorithm and Vector Space Model to generate a data model. Then a query can be applied to find similarities to items that the user has seen. In the end, the recommendation system architecture that was built produced excellent Precision using Recall and Precision testing. Tests are carried out for data using the weighting of product names and product labels. The result obtained 0.84 for the average value of Recall and 0.78 for the average value of Precision.
Transformation of the generalized chaotic system into the discrete-time complex domain Nina Volianska; Roman Voliansky; Oleksandr Sadovoi
Jurnal Informatika Vol 15, No 1 (2021): January 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i1.a20222

Abstract

The paper deals with the development of the backgrounds for the design and implementation of secured communications by using systems with chaotic dynamics. Such backgrounds allow us to perform the stable transformation of a nonlinear object into a simpler form and formulate the nonlinearities simplification optimization algorithm. This algorithm is based on the optimization problem's solution, which makes it possible to define polynomial order, approximation terms, and breakpoints. Usage of proposed algorithms is one of the ways to simplify known chaotic system models without neglecting their unique properties and features. We prove our approach by considering simplifying the Mackey-Glass system and transforming it into a discrete-time complex domain. This example shows that the transformed system produces chaotic oscillations with twice-increased highest Lyapunov exponent. This fact can be considered as improving the unpredictability of the transformed system, and thus it makes background to make highly non-intercepted and undecoded transmission channels.
Application to predict the new student’s score using time series algorithm Sinar Nadhif Ilyasa; Husni Thamrin
Jurnal Informatika Vol 14, No 3 (2020): September 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i3.a17639

Abstract

With the rapid development of information technology in this era, data accuracy is essential in our daily lives to solve existing problems. The existence of information is beneficial in helping the decision-making process. Therefore, any existing information can be further processed and analyzed to be used as new knowledge so that it is useful to determine the right decision. The purpose of this research is to determine whether an application using the time series algorithm such as Auto Regression, ARMA (Auto Regression Moving Average), and Triple Exponential Smoothing model. They can forecast prediction scores that may help to solve the student's admission problem. In this case of the project, the researcher found that the Universitas Muhammadiyah Surakarta's admission system is not evaluated correctly in accepting students and controlling incoming students' quality due to the lack of insights. This time series application is one solution to help manage incoming students' quality and quantity, especially in the Universitas Muhammadiyah Surakarta. This application is developed using a web framework called Django, a full-stack Python web framework that encourages rapid growth and clean, pragmatic design. The Auto Regression model is chosen as a prediction model in One Day Service (ODS) Universitas Muhammadiyah Surakarta. It has a better performance than ARMA and Triple Exponential Smoothing and a higher chance to avoid overfitting than the other two models that are more complex for the ODS data.
Improved data storage performance based modified-SPEED algorithm Mamun Bin Ibne Reaz; Araf Farayez
Jurnal Informatika Vol 15, No 2 (2021): May 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i2.a20610

Abstract

With the rising demand for smart devices and smart home systems, automation and activity prediction has become a vital aspect of people's everyday lives. Researchers have focused on developing approaches that detect user activity patterns and used them to predict future actions. One such system is Modified Sequence Prediction via Enhanced Episode Discovery (M-SPEED), which uses spatiotemporal daily life activities to analyze user behaviors. However, the low accuracy of this algorithm can be a limiting factor inefficient activity prediction. Also, the computational overhead of run time and memory causes this algorithm to show poor performance in large datasets. This research focuses on modifying the M-SPEED algorithm to improve its capability to run on a larger dataset while at the same time improving run time. The accuracy is also improved to make it more effective in real-world applications. Proof of algorithm efficiency is provided to ensure system validity, and simulation is carried out on real-life data. The results demonstrate a 66.69% improvement in cumulative memory efficiency, 37% faster run time, and 8.22% better accuracy confirming the proposal's effectiveness
Performance measurement of the relationship between students' learning with lecturers' characteristics as supervisors based on fuzzy-based assessment Mulyanto Mulyanto; Bedi Suprapty; Arief Bramanto Wicaksono Putra; Achmad Fanany Onnilita Gaffar
Jurnal Informatika Vol 15, No 1 (2021): January 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i1.a17389

Abstract

In addition to the focus of research selected as a Final Project material, the selection of lecturers as student's supervisor becomes very important. The lecturer's competence related to the focus of student research and the supervising style of lecturers is also very influential on the final results. Measurement of style appropriateness between students' learning styles and supervising lecturers' styles can benchmark the quality of the final project's implementation, especially higher education institutions. This study has applied fuzzy-based assessment to build objective perceptions of students' learning characteristics and lecturers' characteristics (Visual (V), Auditory (A), Kinesthetic (K)) as supervisors through questionnaire processing that has designed in such away. Hence, it is suitable for this study. The measuring technique of the percentage of overlapping areas under the curves and the correlation test between a pair of curves have been used as performance measurement metrics. In general, the study results indicate a significant level of coverage adequacy for all research variables regarding existing conditions. It means that the process of Final Project activities in terms of students' and lecturers' learning characteristics as supervisors and their distribution is at a reasonable level (88.38%). It has also been shown by the results of the correlation test of the appropriateness of choice, both supervisors selected by students (0.8657) and students chosen by lecturers (0.9897) who are at a very significant level of similarity. Correlation tests conducted for similarities between students' and lecturers' learning characteristics as supervisors show almost no significant correlation between them (0.4064).
Prediction of standard penetration test value on cohesive soil using artificial neural networks Soewignjo Agus Nugroho; Hendra Fernando; Reni Suryanita
Jurnal Informatika Vol 15, No 2 (2021): May 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i2.a19822

Abstract

Soil investigation is the main key in starting construction. Standard Penetration Test (SPT) and Cone Penetration Test (CPT) are field tests often used to estimate soil parameters for foundation design purposes. The SPT value (N-SPT) shows a correlation between the CPT value and other soil parameters. At present, there have been many conventional correlations examining these correlations, but the nonlinear nature of the soil due to very complex soil formations means that this correlation cannot be used in all situations. This research aimed to predict the value of SPT on cohesive soil based on CPT test data and soil physical properties using artificial neural network capabilities using the Backpropagation algorithm, and the activation function was bipolar sigmoid. This study used 284 data from several places in Sumatra Island, Indonesia, with data input were tip resistance, shaft resistance, effective overburden pressure, percentage of liquid limit, plastic limit, sand, silt, and clay. The results showed that the training data of RMSE was 3.441, MAE and R2 were 0.9451 and 2.318, respectively while test data showed RMSE, MAE, R2 were 2.785, 2.085, and 0.9792, respectively. It means that the proposed artificial neural network NN_Nspt(C) was promising to predict the N-SPT value with a minimum error value and a strong regression equation.

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