The 12S rRNA gene is one of unique regions in mitochondrial genome usually used for phylogenetic studies and species identification. The objective of present study was to develop species specific primers from mitochondrial 12S rRNA gene for identification of dog and rat in beef by using multiplex PCR assay. Three primer pairs of mitochondrial 12S rRNA gene specific for bovine, dog and rat were designed and selected to evaluate their specificity and fidelity. Moreover, a total of twelve DNA samples extracted from meat tissue were also prepared to test those primers using simplex and multiplex PCR. The PCR products were then visualized using 2% of agarose gel under the UV light and three of them were sequenced. In addition, sequence data were analyzed using Clustal Omega software and BLAST. The result showed that simplex PCR assay successfully amplified DNA targets which are respectively indicated by 155 bp (bovine), 244 bp (dog), and 491 bp (rat) of DNA bands. Furthermore, DNA sample sequences were identically similar to reference sequence used in this study. Multiplex and simplex PCR analyses also indicated that these primer pairs specifically amplified DNA target for each species in the samples containing various species. The results suggested that designed primers in this study could be used to identify dog and rat in raw beef containing these species meat. Further experiment should be conducted using meat-processed products and commercial meat products as samples.

Digital Dictionary of Computer and Network Engineering can be used in the learning process of computer and nerwork enginering students. Dictionaries are generally book-shaped is hard to carry because of their thick and heavy, now can be accessed anywhere by development of web technology. Digital dictionaries can be accessed through computers, laptops dan cellphones, this is make student easily in learning process. The digital dictionary can not only be used by students of computer and network engineering, but can also by general public, as long as you have an internet connection, anyone can be use this digital dictionaries. The main process in this digital dictionary is the search process. Binary search algorithm used in the search process of binary search algoritm. Binary search algoritm search is applied to word search in this digital dictionary, because this algoritm is intended for sequintal data. Program language that will be used to build this digital dictionary is PHP and MySQL.

Penjaminan dan peningkatan mutu pendidikan tidak lepas kaitannya dengan manajemen mutu. Berkaitan dengan itu perlu adanya upaya pengendalian mutu (quality control) secara berkelanjutan. Lemahnya komitmen kepala sekolah, guru, tenaga pendidikan dalam melaksanakan sistem penjaminan mutu, serta keterbatasan jumlah kompetensi SDM dalam pemahaman SPMI di SMA Negeri 2 Tebing Tinggi membuat sistem penjaminan mutu sekolah tidak berjalan dengan maksimal.  Kegiatan Pengabdian kepada Masyaraat (PkM) ini bertujuan memberikan pendampingan dan sosialisasi sistem penjaminan mutu pendidikan mengikuti siklus SPMI yang disempurnakan berbasis digital untuk memastikan pelaksanaan SPMI berjalan dengan terus menerus dan berkelanjutan di SMA Negeri 2 Tebing Tinggi serta melakukan pemetaan mutu untuk membangun sistem pengawasan sekolah. Kegiatan PkM Pengembangan Sistem Penjaminan Mutu Sekolah Berbasis Digital dimulai dengan melakukan pemetaan mutu berdasarkan Standar Nasional Pendidikan (SNP), pembuatan Rencana Kerja Sekolah (RKS), pemenuhan mutu baik dalam pengolahan SNP pada proses pembelajaran, monitoring dan evaluasi pelaksanaan, penetapan standar baru, evaluasi hasil implementasi, dan pembuatan sistem penjaminan mutu sekolah berbasis digital. Kegiatan ini menghasilkan luaran berupa sistem penjaminan mutu sekolah berbasis digital yang memuat standar mutu, prosedur operasional baku, kurikulum, RKS, data siswa, guru, prestasi siswa, serta prestasi guru. Kata Kunci : Digital; Penjaminan Mutu; Sistem

The aim of this study was to evaluate color and texture of meatballs made from beef, pork, rat, dog meats, and their mixtures. A total of 32 meatballs have been made and they were grouped into eight treatments containing four meatballs, respectively. Color intensity consisting of lightness(L*), redness (a*), and yellowness (b*) has been tested using chromameter CR-400. Additionally, texture of meatballs consisting hardness, gumminess, springiness, chewiness was analyzed using AMETEX test and calibration instrument. The data obtained from this study was furthermore analyzed using one-way analysis of variance (ANOVA) and the pairwise differences among treatments were tested by Duncan’s multiple range test (DMRT). The results showed that L*, a*, b* were significantly different among treatments (P<0.01). The L* value of dog meat was different with beef, pork, and rat meat. Also, the a* value of beef was similar to pork and significant different with dog and rat meats, while b* value of dog meat was different with other meats. The L*, a*, b* scores in mixture samples consistently represented the average value of color scores of samples consisting various species. Moreover, texture analysis indicated highly significant effect of treatments on hardness, gumminess, springiness, and chewiness (P<0.05). Highest hardness, gumminess, and chewinessscores were found in meatballs made from beefand rat, respectively. In addition, springiness of meatballs was relatively similar among treatments. This study concluded that meatballs made from different species and mixtures had different color and texture. Further study should be conducted to test whether color and texture analysis could be utilized for checking meat-based food adulteration and halalness.

Covid-19 is a virus that was first discovered in China, which has the impact of mild and severe respiratory infections such as pneumonia. Pneumonia is inflammation and consolidation of lung tissue due to infectious agents. Generally pneumonia has a high mortality rate, as do Covid-19 patients. For now, it is very difficult to distinguish between Pneumonia and Covid-19, due to the high similarity of X-Ray image results. The high similarity has an impact on the difficulty of difference between Pneumonia and Covid-19 patients. This research aims to be able to different Pneumonia and Covid-19 patients based on texture analysis of the Gray Level Co-Occurrence Matrix using Modified k-Nearest Neighbour as a classifier. The calculations used in the Gray Level Co-Occurrence Matrix method are Contrast, Correlation, Energy, and Homogeneity which will be input for the Modified k-Nearest Neighbour classifier. The results showed that the highest accuracy is when the value of K = 3 using Manhattan Distance and 80%:20% data percentage, which is 87.5%. For the values of K = 7 and K = 9 there is no change in accuracy, so it can be concluded that the value of K that affects accuracy only occurs at the values of K = 3 and K = 5. Then, the higher the K value, the lower the resulting accuracy.

Dalam memberikan pelayanan sistem antrian pasien di rumah sakit masih banyak ditemukan kendala dalam proses antrian, diantaranya  penumpukan pasien saat mengambil nomor antrian, antrian yang terlalu banyak dalam sistem antrian dan lamanya waktu menunggu dalam sistem antrian sehingga proses antrian dianggap memperlambat proses pelayanan. Oleh sebab itu penulis mengembangkan sistem antrian yang dilengkapi dengan jalur masuk pelayanan yang banyak dan sistem pelayanan yang banyak yang disebut dengan sistem antrian multi channel – multi phase. Penerapan model antrian multi channel – multi phase  membuat aplikasi dapat melayani lebih dari satu jenis layanan di rumah sakit dan dengan jalur masuk yang banyak yang dapat di sesuaikan dengan jenis dan jumlah unit pelayanan. Aplikasi  antrian  ini  juga  dilengkapi  dengan  pemanggil suara seperti aplikasi antrian yang telah ada saat ini dan bisa dilakukan perubahan atau penambahan jumlah dan jenis layanan sewaktu-waktu sesuai kebutuhan rumah sakit. Keywords – queue, hospital, multi-channel multi-phase.

Exam questions made by lecturers in general in an educational institution around 99% are made from Microsoft Word applications. This happens because of the ability or skill of the question maker and the facilities of the institution. The exam questions that have been made seem like a consumable model, even though the material from these questions still has a relationship with the subject. With this condition the existing questions are not necessary and do not have to be destroyed, it should be stored and made into a question bank. So on the next examination the test questions stored in the bank questions to be randomized again to make new questions as many as the number of questions needed so that the exam questions are presented far from static and color and each test for each participant will never be the same as previous questions. and also the implementation of semi-computerized exams do not need to require a computer laboratory. This application is equipped with a randomization method, namely Linear Congruent Method (LCM), so that the questions that appear will never be the same each time printing a question in the form of executable (EXE), making it easier for study programs to make exam questions in multiple choices and in accordance with existing material in the syllabus and GBPP that apply from several lecturers who teach the same subject.

Indonesia is an agricultural country that is famous for its wealth of spices and herbal plants. Herbal plants themselves have thousands of species. There are 40,000 species of herbal plants that have been known in the world, and around 30,000 species to be in Indonesia. Herbal plants are a source of new active compounds that have pharmacological and therapeutic effects, both when used directly and through various extraction processes. Herbal plants can be distinguished from the shape of the leaves because each type of plant has different leaf features. Laboratory-based testing also requires skills in sample processing and data interpretation, in addition to timeconsuming procedures. Therefore, a simple and reliable herbal plant recognition technique is needed to quickly identify herbs, especially for those who are unable to use expensive analytical instrumentation. This study aims to identify types of herbal plants based on leaf images quickly and accurately using the Convolutional Neural Network method which is part of Deep Learning. This study uses several architectural models of Convolutional Neural Network to classify types of herbal plants. The best accuracy value with the VGG16 architecture is 90% with 93% precision, 90% recall, and 90% Fmeasure. The VGG16 architecture used epoch = 20, batch_size = 32, and validation_split = 0.2. The result show that CNN Algorithm with the VGG16 architecture is able to classify types of herbal plants with good accuracy.